CN103313354B - Based on the heterogeneous network system of selection of four kinds of weight vectors - Google Patents

Based on the heterogeneous network system of selection of four kinds of weight vectors Download PDF

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CN103313354B
CN103313354B CN201310192317.5A CN201310192317A CN103313354B CN 103313354 B CN103313354 B CN 103313354B CN 201310192317 A CN201310192317 A CN 201310192317A CN 103313354 B CN103313354 B CN 103313354B
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朱琦
张硕
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南京邮电大学
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Abstract

The present invention is a kind of heterogeneous network system of selection based on four kinds of weight vectors, and the method obtains a comprehensive weight vector by comprehensive four network attribute weights, and ensures its reasonability by consistency check.Then network selection is carried out in conjunction with SAW method.The method takes into full account current network conditions and can provide gratifying service quality according to type of service.Concrete steps are as follows: first give objective parameter and calculate objective network attribute weight vector by Information Entropy and dispersion method, give subjective parameters and obtain respective subjective network attribute weight vector respectively by AHP method and G-1 method, four kinds of methods represent four policymaker, utilize Group Decision theoretical by weight vector new for four kinds of weight vector combination producings, thus obtain a comprehensive weight vector W, finally carry out network selection in conjunction with SAW method.

Description

Based on the heterogeneous network system of selection of four kinds of weight vectors

Technical field

The present invention relates to a kind of heterogeneous network system of selection based on four kinds of weight vectors, belong to communication technical field.

Background technology

Mobile communication experienced by the first generation analog cellular system 1G from providing analog voice business, adopt the second generation mobile communication system 2G of digital modulation to the process of 3-G (Generation Three mobile communication system) 3G taking CDMA as mainstream technology, along with the development of radio communication, following communication network will be the heterogeneous network comprising various wireless access technology, such as 2G, 3G, UMTS, WLAN, WiMAX, WiFi etc., the often overlapped covering of these dissimilar wireless networks, there is no unified standard interface, and they are in coverage, access rate, capacity, business and mobility feature, there is bigger difference in the aspects such as network quality, respectively have superiority again, between heterogeneous network, seamless access is realized as by multi-interface terminal, user can select UMTS with the service quality obtained, or select WiMAX to obtain high data rate, or select WLAN to obtain lower cost, this just needs the advantage making full use of each network, so the fusion of heterogeneous network is the development trend of future network communication.

It is the committed step realizing the network integration that heterogeneous network is selected.And multiple attributive decision making method MADM (MultipleAttributesDecisionMaking) is one of most effective method in heterogeneous network system of selection.Classical MADM method comprises simple weighted method SAW, multiplication index weights method MEW, sort method TOPSIS, gray relative analysis method GRA, VIKOR method and superseded back-and-forth method ELECTRE etc. close to ideal value.These multiple attributive decision making methods all can relate to multiattribute weight vectors, the vector of multiattribute weight, generally will consider the objective attribute of network, also should take into full account user preference and type of service.Conventional objective weighted model comprises entropy assessment EW(EntropyMethod) and dispersion method etc.; Classical subjective weighting method comprises analytic hierarchy process AHP (AnalyticHierarchyProcess) and G-1 method.

Subjective and objective weight integrates by a lot of method carries out network selection." a kind of heterogeneous network selection algorithm of multiple attribute decision making (MADM) " that " radio engineering " the 39th volume the 1st periodical in 2009 carries, author: Wang Kang, Zeng Zhimin, Feng Chunyan, Zhang Tiankui (Beijing University of Post & Telecommunication's communication network comprehensive technology research institute), propose to adopt AHP to ask subjective weight, entropy assessment asks objective weight, then by the two linear weighted function, provides a kind of network selecting method in conjunction with SAW method, but what the method did not provide linear weighted function coefficient asks method, and namely the determination of weight vectors is too random.What " InformationComputingandApplicationsLectureNotesinCompute rScience " published be numbered " 6377/2010:213-220 " document " ANovelAHPandGRABasedHandoverDecisionMechanisminHeterogen eousWirelessNetworks " proposes the network selecting method of a kind of AHP in conjunction with GRA, this method application complexity is comparatively large, and does not have enough theory support.And although above-mentioned two kinds of methods are subjective and objective takes into account, the objective circumstances of network can be considered and can user preference be taken into account, can type of service be taken into full account.The document " Networkselectionbasedonmultipleattributedecisionmakingan dgroupdecisionmakingforheterogeneouswirelessnetworks " that what " Chinese post and telecommunications journal " in October, 2012 published be numbered " 19(5): 92-98 " adopts AHP to ask subjective weight, entropy assessment asks objective weight, then Group Decision is carried out to the two, obtain a kind of balance algorithm, but when only adopting two policymaker, policymaker participates in quantity very little, the feature making Group Decision can not make full use of it to gather multiple policymaker's wisdom.

Summary of the invention

Technical problem: the object of this invention is to provide a kind of heterogeneous network system of selection based on four kinds of weight vectors, the multiple attribute weight vector that the method utilizes multiple attributive decision making method to obtain, theoretical based on Group Decision, they are combined thus obtains a geometric generalization weight vector.If this result does not meet compatibility requirements, then need the judgement matrix revising subjective weighting method, again calculate, until obtain the comprehensive weight vector that meets compatibility requirements, then carry out network selection in conjunction with SAW method, the method can provide gratifying service quality for different service types.

The present invention is directed to the deficiencies in the prior art, propose a kind of method of the combining weights based on Group Decision of improvement.First obtain respective subjective network attribute weight vector respectively by G-1 method and AHP method, then utilize Information Entropy and dispersion method to calculate respective objective network attribute.Four kinds of methods represent four policymaker, two subjective decision persons, two objective making decision persons, and policymaker's quantity is moderate and representative, also avoids the problem of said method simultaneously.

Technical scheme: the present invention and a kind of heterogeneous network system of selection based on four kinds of weight vectors, by the wisdom of comprehensive multiple policymaker, consider network objective attribute, user preference and type of service, and more gratifying network can be provided to select according to type of service.

Based in the heterogeneous network system of selection of four kinds of weight vectors, first network attribute weight to be obtained.Multiple attribute decision making (MADM) generally all can relate to the determination of weight vectors, and the defining method of traditional weight vectors can be divided into subjective weighting method and objective weighted model.Subjective weighting method comprises AHP, G-1 method and Delphi method etc.; Objective weighted model comprises Information Entropy, dispersion method, CRITIC method etc.Two class enabling legislations emphasize particularly on different fields and feature.Therefore, in order to consider the factors such as type of service, user preference, network objective attribute, and make full use of Group Decision balance and gather the feature of multiple policymaker's conclusion, comprehensive multiple subjective and objective weight is obtained multiattribute weight vectors by this method.Wherein subjective weighting method adopts AHP and G-1 method, and objective weighted model adopts Information Entropy and dispersion method.After using four kinds of enabling legislations to obtain four groups of weight vectors, obtain a comprehensive weight vector by Group Decision, then weigh its compatibility to judge the reasonability of comprehensive weight vector.The comprehensive weight vector meeting compatibility will carry out network selection in conjunction with SAW method.

Objective weighted model adopts Information Entropy and dispersion method.Under heterogeneous network model of the present invention, the property parameters that network relates in selecting is available bandwidth, hardware circuit solution, packet delay, packet jitter, packet loss and every bit expense, wherein: B represents bandwidth, R represents hardware circuit solution, D represents packet delay, J represents packet jitter, L represents packet loss, C represents every bit expense.The objective weight of attribute can be gone out according to network attribute calculation of parameter.

Heterogeneous network model of the present invention contains N kind heterogeneous network.Suppose that the network attribute matrix of the method is in Information Entropy and dispersion method:

R=(x ij) n × 6, i.e. N number of heterogeneous network number, 6 kinds of network attributes.

X ijrepresent a jth attribute of i-th network, as:

X i1represent the bandwidth of i-th network;

X i2represent the hardware circuit solution of i-th network;

X i3represent the time delay of i-th network;

X i4represent the shake of i-th network;

X i5represent the packet loss of i-th network;

X i6represent every bit expense of i-th network;

And 1<=i<=N, 1<=j<=6.

Generally speaking, attribute is divided into profit evaluation model and cost type, and the attribute of profit evaluation model is the bigger the better, and gets x max j = max ( x 1 j , x 2 j , . . . , x Nj ) ;

The attribute of cost type is the smaller the better, gets x min j = min ( x 1 j , x 2 j , . . . , x Nj ) .

For B and R, they belong to profit evaluation model attribute, and standardization formula is:

r ij = x ij x max j + x min j , 1 &le; j &le; 2

For D, J, L and C, they belong to cost type attribute, and standardization formula is:

r ij = x max i + x mim i - x ij x max j + x min j , 3 &le; j &le; 6

The weight vectors of attribute can be obtained by Information Entropy:

W EW = ( w 1 EW , w 2 EW , w 3 EW , w 3 EW , w 4 EW w 5 EW w 6 EW )

The weight vectors utilizing dispersion method to calculate is:

W &sigma; = ( w 1 &sigma; , w 2 &sigma; , w 3 &sigma; , w 4 &sigma; , w 5 &sigma; , w 6 &sigma; )

Subjective weighting method of the present invention adopts AHP method and G-1 method.In the judgment matrix of AHP, each element is an Attribute Relative in another ratio with the weight of the attribute under rule layer, and AHP can be adjudicated matrix and be designated as B, then B matrix is write as following form:

B = b 11 b 12 . . . b 1 n b 21 b 22 . . . b 2 n . . . . . . . . . . . . b n 1 b n 2 . . . b nn = w 1 / w 1 w 1 / w 2 . . . w 1 / w n w 2 / w 1 w 2 / w 2 . . . w 2 / w n . . . . . . . . . . . . w n / w 1 w n / w 2 . . . w n / w n

Wherein: n is network attribute parameter sum, b ijrepresent that i-th Attribute Relative is in the significance level of a jth attribute.About how to determine b ijvalue, Saaty etc. advise that reference numerals 1-9 and inverse thereof are as scale.The implication that table 1 lists 1-9 scale represents based on the individual character of optant, experience and knowledge and the Selecting parameter of the significance level of carrying out.

From psychological perspective, classification can surmount the judgement of people too much, has both added the difficulty doing to judge, and easily therefore provides false data again.The people such as Saaty also experimentally compare the correctness of people's judged result under various different scale, and experimental result also shows, adopt the scale of 1-9 the most suitable.

Table 1.Saaty scale

Sequence number Importance rate b ijAssignment 1 I, j two elements are of equal importance 1 2 I element is more important a little than j element 3 3 I element is obviously more important than j element 5 4 I element is strongly more important than j element 7 5 I element is extremely more important than j element 9 6 I element is slightly more inessential than j element 1/3 7 I element is obviously more inessential than j element 1/5 8 I element is strongly more inessential than j element 1/7 9 I element is extremely more inessential than j element 1/9

According to the difference of type of service, the judgement matrix of subjective weighting method can change.Judgment matrix in AHP method is A aHP, the judgment matrix of G-1 method is G g-1.Therefore under often kind of type of service, in conjunction with the relative importance in practical application between each attribute, and in conjunction with Saaty scale, A can be obtained aHPand G g-1, the dissimilar judgement matrix of AHP and G-1 method provides respectively in table 2 and table 3.

AHP method determines subjective weight: correspond to often kind of type of service, associative list 2, calculates weight step, under particular traffic type condition, can calculate the subjective weight that AHP method is determined according to AHP:

W AHP = ( w 1 AHP , w 2 AHP , w 3 AHP , w 4 AHP , w 5 AHP , w 6 AHP )

G-1 method determines that subjective weight is divided into three steps, first carries out importance ranking according to certain evaluation criterion to all evaluation indexes, and then the significance level ratio of adjacent index after given sequence, finally calculates the weight of each index.The subjective weight vector that G-1 method under certain particular traffic type is determined is:

W G - 1 = ( w 1 G - 1 , w 2 G - 1 , w 3 G - 1 , w 4 G - 1 w 5 G - 1 w 6 G - 1 )

Group Decision plays the advantage of group decision-making by concentrated group member wisdom, and therefore selected group member should be representative and will ensure some.Four kinds of decision-making members selected by this method are respectively analytic hierarchy process (AHP), Information Entropy, G-1 method and dispersion method, four kinds of decision-making techniques comprise two kinds of subjective weighting method, two kinds of objective weighted models, quantity is moderate, obtained the advantage of Group Decision when network attribute weight parameter can not only utilize multiple policymaker to participate in by Group Decision, more can take into full account that user requires, type of service and network condition.

If A=(a ij), B=(b ij) and C=(c ij) being n rank positive reciprocal matrix, the ordering vector obtained by A is W=(w 1, w 2..., w n) t, matrix W=(w i/ w j) be called the eigenmatrix of A.Product C (A, the B)=e of definition A and B t* A*B*e is the compatible degree of A, B, wherein e t=(1,1 ... 1).

Conveniently, generally get its logarithm as compatible degree, be designated as: general, LC(A, B)>=0, if LC(A, B)=0, then A, B are completely compatible.

Four kinds of enabling legislations are based on same network paramter matrix, and the ordering vector determined by these four kinds of enabling legislations is respectively W eW, W σ, W aHPand W g-1, for convenience of calculating, note:

W ew = W 1 = ( w 1 1 , w 2 1 , w 3 1 , w 4 1 , w 5 1 , w 6 1 )

W &sigma; = W 2 = ( w 1 2 , w 2 2 , w 3 2 , w 4 2 , w 5 2 , w 6 2 )

W AHP = W 3 = ( w 1 3 , w 2 3 , w 3 3 , w 4 3 , w 5 3 , w 6 2 )

W G - 1 = W 4 = ( w 1 4 , w 2 4 , w 3 4 , w 4 4 , w 5 4 , w 6 4 )

Four ordering vector characteristic of correspondence matrixes are respectively A eW, A σ, A aHPand A g-1.Geometric generalization average vector W=(w 1, w 2, w 3, w 4, w 5, w 6) be make A under logarithm meaning eW, A σ, A aHPand A g-1with comprehensive characteristics matrix W=(w i/ w j) the most compatible vector, even if get the vector of minimum value.If W=is (w 1, w 2, w 3, w 4, w 5, w 6) make P get minimum value, so should have

Because solution formula obtains:

w t = ( w t EW w t &sigma; w t AHP w t G - 1 ) / &Sigma; t = 1 6 ( w t EW w t &sigma; w t AHP w t G - 1 ) 1 / 4 = ( w t 1 w t 2 w t 3 w t 4 ) / &Sigma; t = 1 6 ( w t 1 w t 2 w t 3 w t 4 ) 1 / 4

In conjunction with above-mentioned formula, obtain the geometric generalization average vector of four kinds of weight vectors:

W=(w 1,w 2,w 3,w 4,w 5,w 6)

Comprehensive characteristics matrix:

A=[(w i/w j)],1≤i,j≤6

The method is index S I(A, B)=C (A, B)/n 2be called matrix A, the compatibility index of B.

Usually, A and W has compatibility, but the compatibility of A and W is determined by A completely, and works as think that A and W has satisfied compatibility.Therefore for the ease of judging, the method is got as the boundary value of compatibility index, boundary value provides in table 5.

When whether compatiblely weighing A and B, when time the method think that A and B has gratifying compatibility.

For ensureing the reasonability of mix vector, the method carries out consistency check to synthetic weight, namely judgment matrix A whether with A eW, A σ, A aHP, A g-1have satisfied compatibility respectively, their compatibility index is respectively:

SI 1 = SI ( A , A EW ) = C ( A , A EW ) / 6 2 &Sigma; i = 1 M &Sigma; j = 1 M ( w j / w j ) ( w j EW / w i EW ) = &Sigma; i = 1 M ( w i / w i EW ) &Sigma; j = 1 M ( w j EW / w j ) / 6 2

SI 2 = SI ( A , A &sigma; ) = C ( A , A &sigma; ) / 6 2 = &Sigma; i = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j &sigma; / w i &sigma; ) = &Sigma; i = 1 M ( w i / w i &sigma; ) &Sigma; j = 1 M ( w j &sigma; / w j ) / 6 2

SI 3 = SI ( A , A G - 1 ) = C ( A . A G - 1 ) / 6 2 = &Sigma; j = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j G - 1 / w i G - 1 ) = &Sigma; i = 1 M ( w i / w i G - 1 ) &Sigma; j = 1 M ( w j G - 1 / w j ) / 6 2

SI 4 = SI ( A , A AHP ) = C ( A , A AHP ) / 6 2

When four compatibility indexes are less than same order compatibility index critical value simultaneously then illustrate that the weight of synthesis meets compatibility requirements.The weight vector utilizing Group Decision to obtain is to carry out network selection, and after namely obtaining weight, in conjunction with SAW method, the performance function of each network can be expressed as:

Optimum network is: F * = arg max i &Element; N &Sigma; j &Element; 6 w j r ij .

The content application of Group Decision in network selection procedures, is considered network objective attribute by the present invention, user's request and type of service.

Beneficial effect:

1. when adopting Group Decision theory to obtain integrated network attribute weight, adopt four policymaker, quantity is moderate and representative respectively, comprehensive weight ensures its reasonability by united Lagrangian-Eulerian method inspection, namely the determination of weight is clear and definite and have abundant theory support, and correctly making full use of combinatorial theory, network selection procedures is unlikely to again undue complexity.

2. reach by comprehensive multiple policymaker's wisdom the impact considering network objective attribute, user preference and type of service in network selection procedures.And can according to the difference of type of service by given different judgement matrix for the user under different service types provides satisfied QoS.

Accompanying drawing explanation

Fig. 1 is that network of the present invention selects block diagram.

Fig. 2 is method particular flow sheet of the present invention.

Fig. 3 is the time delay simulation figure of session service.

Fig. 4 is the judder figure of session service.

Fig. 5 is the throughput analogous diagram of session service.

Fig. 6 is the judder figure of streaming media service.

Fig. 7 is the packet loss analogous diagram of streaming media service.

Fig. 8 is the throughput analogous diagram of streaming media service.

Fig. 9 is the packet loss analogous diagram of interactive service.

Figure 10 is the throughput analogous diagram of interactive service.

Figure 11 is the cost analogous diagram of interactive service.

Figure 12 is the cost analogous diagram of background business.

Figure 13 is the packet loss analogous diagram of background business.

Embodiment

Be described in detail below in conjunction with the technical scheme of accompanying drawing to invention:

Thinking of the present invention Group Decision and united Lagrangian-Eulerian method is applied to the weight solving each property parameters that network is selected in heterogeneous network vertical handover procedure, this weighted basis sorts to network and the network selecting cost function maximum as shown in Figure 1, first give objective parameter and calculate objective network attribute weight vector by Information Entropy and dispersion method, give subjective parameters and obtain respective subjective network attribute weight vector respectively by AHP method and G-1 method, four kinds of methods represent four policymaker, utilize Group Decision theoretical by weight vector new for four kinds of weight vector combination producings, thus obtain a comprehensive weight vector W, finally carry out network selection in conjunction with SAW method.

The detail flowchart that whole network switching process adopts Group Decision method to realize network selection is shown in Fig. 2.

One, the determination of objective weight

Heterogeneous network model of the present invention contains N kind heterogeneous network, the property parameters that network relates in selecting is available bandwidth, hardware circuit solution, packet delay, packet jitter, packet loss and every bit expense, in this method, B represents bandwidth, R represents hardware circuit solution, and D represents packet delay, J represents packet jitter, L represents packet loss, C represents every bit expense.Suppose that the standardized network attribute matrix in Information Entropy and dispersion method is:

R=(x ij) n × 6, i.e. N number of heterogeneous network number, 6 kinds of network attributes.Wherein:

X ijrepresent a jth attribute of i-th network, as:

X i1represent the bandwidth of i-th network;

X i2represent the hardware circuit solution of i-th network;

X i3represent the time delay of i-th network;

X i4represent the shake of i-th network;

X i5represent the packet loss of i-th network;

X i6represent every bit expense of i-th network, and 1<=i<=N, 1<=j<=6.

Generally speaking, attribute is divided into profit evaluation model and cost type, and the attribute of profit evaluation model is the bigger the better: get x max j = max ( x 1 j , x 2 j , . . . , x Nj ) ;

The attribute of cost type is the smaller the better: get x min j = min ( x 1 j , x 2 j , . . . , x Nj ) .

For B and R, they belong to profit evaluation model attribute, and standardization formula is:

r ij = x ij x max j + x min j , 1 &le; j &le; 2 - - - ( 1 )

For D, J, L and C, they belong to cost type attribute, and standardization formula is:

r ij = x max i + x min i - x ij x max j + x min j , 3 &le; j &le; 6 - - - ( 2 )

Information Entropy determination objective weight:

(1) standardization

r &OverBar; ij = r ij / &Sigma; i = 1 6 r ij , 1 &le; j &le; 6 - - - ( 3 )

(2) comformed information entropy

H i = - K &Sigma; j = 1 6 r &OverBar; ij 1 n r &OverBar; ij , 1 &le; i &le; N - - - ( 4 )

Here: K=1/lnN

(3) weight vectors is calculated

w j EW = 1 - H i 6 - &Sigma; j = 1 6 H i , 1 &le; i &le; N - - - ( 5 )

The weight vectors of attribute can be obtained by Information Entropy:

W EW = ( w 1 EW , w 2 EW , w 3 EW , w 4 EW , w 5 EW , w 5 EW , w 6 EW )

Dispersion method determination objective weight: dispersion method is similar with Information Entropy Computing Principle.Generally in certain network, the standard deviation of an index is directly proportional to the degree of variation of desired value, if standard deviation is larger, then the degree of variation of this index is larger, and the amount of information provided is larger, plays a role larger in evaluation, and its weight is also larger.Otherwise then weight is less.The present invention calculate six networks unify attribute difference formula be:

&sigma; j = &Sigma; i = 1 N ( r ij - r &OverBar; j ) 2 N , r &OverBar; j = &Sigma; i = 1 N r ij 6 , 1 &le; j &le; 6 - - - ( 6 )

Dispersion method is utilized to calculate each index weights formula of N number of heterogeneous network as follows:

w j &sigma; = &sigma; j &Sigma; j = 1 6 &sigma; j , j = 1,2 , . . . , 6 - - - ( 7 )

Then network attribute matrix R=(r ij) n × 6the weight vectors utilizing dispersion method to calculate is:

W &sigma; = ( w 1 &sigma; , w 2 &sigma; , w 3 &sigma; , w 4 &sigma; , w 5 &sigma; , w 6 &sigma; ) .

Two, the determination of subjective weight

Subjective weighting method of the present invention adopts AHP method and G-1 method.According to the difference of type of service, the judgement matrix of subjective weighting method can change.Judgment matrix in AHP method is A aHP, judging of G-1 method is vectorial as G g-1, the dissimilar judgement matrix of AHP and G-1 method provides respectively in table 2 and table 3.The data of adjudicating matrix in table provide according to relative importance between the corresponding parameter of the given difference according to type of service of expert, and provide successively in conjunction with Saaty scale.

Table 2 is the judgment matrix that in AHP method, different service types is corresponding

Table 3 is the judgment matrix that in G-1 method, different service types is corresponding

Type of service r 2 r 3r 4r 5 r 6 Sequence situation Session service 1.2 1.611.2 1.2 R>J>B>C>P>L Streaming media service 1.8 111 1 L>B>P>R>J>C Interaction service 1 1.811.4 1 L>C>R>J>B>P Background business 1.2 1.411 1.2 C>L>J>R>B>R

Therefore under often kind of type of service, A aHPand G g-1corresponding with adjudicating matrix in table 2 and table 3 respectively.AHP method determines subjective weight: correspond to often kind of type of service, associative list 2, calculates weight step, under particular traffic type condition, can calculate the subjective weight that AHP method is determined according to AHP:

W AHP = ( w 1 AHP , w 2 AHP , w 3 AHP , w 4 AHP , w 5 AHP , w 6 AHP )

G-1 method determines subjective power: G-1 method determines that subjective weight is divided into three steps, first carries out importance ranking according to certain evaluation criterion to all evaluation indexes, and then the significance level ratio of adjacent index after given sequence, finally calculates the weight of each index.Concrete steps are as follows:

(1) determination of order relation

To all evaluation indexes relative to the sequence of certain evaluation criterion significance level, then the importance ranking situation of six attribute under often kind of type of service under this heterogeneous network model is as follows:

Session service: R>J>B>CGreatT. GreaT.GTP>L

Streaming media service: L>B>P>RGreatT. GreaT.GTJ>C

Interaction service: L>C>R>JGreatT. GreaT.GTB>P

Background business: C>L>J>RGreatT. GreaT.GTB>R

(2) adjacent relative significance of attribute degree judges

Under the significance level sequence of certain evaluation criterion, between adjacent attribute, the ratio of significance level is:

w k-1/w k=r k,2≤k≤6(8)

R kassignment can refer to table 4, and table 4 comes from the works " comprehensive evaluation theory, method and application " that G-1 method author is Guo second place, Science Press in May, 2007.Author defines the assignment rule of this r value when introducing G-1 method.

R value in table 3 under miscellaneous service type is that associative list 2 and table 4 provide, namely according to the relative importance under given each type of service of expert between each attribute, r value assignment rule, provides successively, thus ensures that the subjective judgement for all types of service is consistent.

Table 4 is r value assignment table in G-1 method

r k Explanation 1.0 Index x k-1And x kThere is equal importance 1.2 Index x k-1Compare x kImportant a little 1.4 Index x k-1Compare x kObviously important 1.6 Index x k-1Compare x kStrongly important 1.8 Index x k-1Compare x kExtremely important

(3) determination of weight

First most important attribute weight W after calculating importance ranking 6, then calculate follow-up weight successively.

Weight can have following formula to determine:

w 6 = 1 1 + &Sigma; k = 2 6 &Pi; i = k 6 r i - - - ( 9 )

Follow-up weight can be drawn by step (2):

w k-1=r k×w k,k=6,5,...,2(10)

In conjunction with above-mentioned formula, and with reference to r value in table 3, the subjective weight vector that the G-1 method under certain particular traffic type is determined is:

W G - 1 = ( w 1 G - 1 , w 2 G - 1 , w 3 G - 1 , w 4 G - 1 , w 5 G - 1 , w 6 G - 1 )

Three, Group Decision and consistency check

Group Decision plays the advantage of group decision-making by concentrated group member wisdom, and therefore selected group member should be representative and will ensure some.Four kinds of decision-making members selected by the present invention are respectively analytic hierarchy process (AHP), Information Entropy, G-1 method and dispersion method, four kinds of decision-making techniques comprise two kinds of subjective weighting method, two kinds of objective weighted models, quantity is moderate, obtained the advantage of Group Decision when network attribute weight parameter can not only utilize multiple policymaker to participate in by Group Decision, more can take into full account that user requires, type of service and network condition.Next Group Decision and united Lagrangian-Eulerian method is used to combine four kinds of weight vectors introduction.

If A=(a ij), B=(b ij) and C=(c ij) being n rank positive reciprocal matrix, the ordering vector obtained by A is W=(w 1, w 2..., w n) t, matrix W=(w i/ w j) be called the eigenmatrix of A.Product C (A, the B)=e of definition A and B t* A*B*e is the compatible degree of A, B, wherein e t=(1,1 ... 1).Conveniently, generally get its logarithm as compatible degree, be designated as general, LC(A, B)>=0, if LC(A, B)=0, then A, B are completely compatible.

Four kinds of enabling legislations are based on same network paramter matrix, and the ordering vector determined by these four kinds of enabling legislations is respectively W eW, W σ, W aHPand W g-1, for convenience of calculating, note:

W ew = W 1 = ( w 1 1 , w 2 1 , w 3 1 , w 4 1 , w 5 1 , w 6 1 )

W &sigma; = W 2 = ( w 1 2 , W 2 2 , w 3 2 , w 4 2 , w 5 2 , w 6 2 )

W AHP = W 3 = ( w 1 3 , w 2 3 , w 3 3 , w 4 3 , w 5 3 , w 6 3 )

W G - 1 = W 4 = ( w 1 4 , w 2 4 , w 3 4 , w 4 4 , w 5 4 , w 6 4 )

Four ordering vector characteristic of correspondence matrixes are respectively A eW, A σ, A aHPand A g-1.Geometric generalization average vector W=(w 1, w 2, w 3, w 4, w 5, w 6) be make A under logarithm meaning eW, A σ, A aHPand A g-1with comprehensive characteristics matrix W=(w i/ w j) the most compatible vector, even if get the vector of minimum value.If W=is (w 1, w 2, w 3, w 4, w 5, w 6) make P get minimum value, so should have:

&PartialD; P &PartialD; t = 0,1 &le; t &le; 6 - - - ( 11 )

Because solution formula obtains:

w t = ( w t EW w t &sigma; w t AHP w t G - 1 ) / &Sigma; t = 1 6 ( w t EW w t &sigma; w t AHP w t G - 1 ) 1 / 4 = ( w t 1 w t 2 w t 3 w t 4 ) / &Sigma; t = 1 6 ( w t 1 w t 2 w t 3 w t 4 ) 1 / 4 - - - ( 12 )

In conjunction with above-mentioned formula, obtain the geometric generalization average vector of four kinds of weight vectors:

W=(w 1,w 2,w 3,w 4,w 5,w 6)

Comprehensive characteristics matrix: A=[(w i/ w j)], 1≤i, j≤6

The method is index S I(A, B)=C (A, B)/n 2be called matrix A, the compatibility index of B.Usually, A and W has compatibility, but the compatibility of A and W is determined by A completely, and works as think that A and W has satisfied compatibility.Therefore for the ease of judging, the method is got as the boundary value of compatibility index, boundary value provides in table 5.Table 5 is " compatibility and Group Decision " that " system engineering theory with put into practice " is published in February, 2000 (the 2nd phase), Renmin University of China Wang Lian is fragrant, wherein calculate, the present invention only need use the reasonability of data checking method process variable in table 5, therefore directly quotes its data at this.

Table 5 is S.I. critical value

When whether compatiblely weighing A and B, when time the method think that A and B has gratifying compatibility.

For ensureing the reasonability of mix vector, carry out consistency check to synthetic weight, namely whether judgment matrix A is and A eW, A σ, A aHPand A g-1have satisfied compatibility respectively, their compatibility index is respectively:

SI 1 = SI ( A , A EW ) = C ( A , A EW ) / 6 2 &Sigma; i = 1 M &Sigma; j = 1 M ( w j / w j ) ( w j EW / w i EW ) = &Sigma; i = 1 M ( w i / w i EW ) &Sigma; j = 1 M ( w j EW / w j ) / 6 2 - - - ( 13 )

SI 2 = SI ( A , A &sigma; ) = C ( A , A &sigma; ) / 6 2 = &Sigma; i = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j &sigma; / w i &sigma; ) = &Sigma; i = 1 M ( w i / w i &sigma; ) &Sigma; j = 1 M ( w j &sigma; / w j ) / 6 2 - - - ( 14 )

SI 3 = SI ( A , A G - 1 ) = C ( A , A G - 1 ) / 6 2 = &Sigma; j = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j G - 1 / w i G - 1 ) = &Sigma; i = 1 M ( w i / w i G - 1 ) &Sigma; j = 1 M ( w j G - 1 / w j ) / 6 2 - - - ( 15 )

SI 4 = SI ( A , A AHP ) = C ( A , A AHP ) / 6 2 - - - ( 16 )

When four compatibility indexes are less than same order compatibility index critical value simultaneously then illustrate that the weight of synthesis meets compatibility requirements.

Four, network is selected

The geometric generalization weight vector meeting compatibility requirements utilizing Group Decision to obtain is to carry out network selection, and after namely obtaining weight, in conjunction with SAW method, the performance function of each network can be expressed as:

F = &Sigma; j = 1 6 w j r ij , 1 &le; i &le; N - - - ( 17 )

Optimum network is:

F * = arg max i &Element; N &Sigma; j &Element; 6 w j r ij - - - ( 18 )

In sum, usefulness of the present invention is provided by simulation result:

Heterogeneous network in emulation is WLAN, UMTS and WIMAX respectively, and every type comprises two networks.The present invention is in simulations compared with the prior art: with " a kind of heterogeneous network selection algorithm of multiple attribute decision making (MADM) " that " EW " representative " radio engineering " the 39th volume the 1st periodical in 2009 carries in analogous diagram, author: Wang Kang, Zeng Zhimin, Feng Chunyan, method in Zhang Tiankui (Beijing University of Post & Telecommunication's communication network comprehensive technology research institute), by the method be numbered in " 6377/2010:213-220 " document " ANovelAHPandGRABasedHandoverDecisionMechanisminHeterogen eousWirelessNetworks " that " GRA " representative " InformationComputingandApplicationsLectureNotesinCompute rScience " publishes in analogous diagram.The property parameters that network relates in selecting is available bandwidth (AvailableBandwidth, B), hardware circuit solution (PeakDataRate, R), packet delay (PacketDelay, D), packet jitter (PacketJitter, J), packet loss (PacketDelay, D) with every bit expense (CostPerBit, C), shown in table 6.And three kinds of heterogeneous networks in table 6, i.e. UMTS, WLAN and WiMAX, the distribution of their six attribute provides according to the network attribute data of adding up in practical application.

Table 6 is network attribute parameter

Property parameters UMTS1 UMTS2 WLAN1 WLAN2WiMAX1WiMAX2 B(MHz) 0.1-2 0.1-2 1-11 1-541-601-60 R(Mbps) 2 2 11 546060 D(ms) 25-50 25-50 100-150 100-15060-10060-100 J(ms) 5-10 5-10 10-20 10-203-103-10 L(per10 6) 20-80 20-80 20-80 20-8020-8020-80 C(price) 0.6 0.8 0.1 0.050.50.4

Select 4 kinds of types of service to carry out measure algorithm performance in emulation, concrete simulation result is as shown in Fig. 3-Figure 13.

Fig. 3-Fig. 5 gives the performance of session service.The voice communication of session service requires low time delay and lower bandwidth, and video communication requires low time delay and enough bandwidth, and in the judgement matrix of therefore table 2 and table 3, session service more values delay variation situation.As can be seen from the figure, contrast other two kinds of methods, the inventive method can provide optimum delay time and jitter performance and gratifying bandwidth, thus can meet the qos requirement of user.

Fig. 6-Fig. 8 gives the performance of streaming media service.Streaming media service requires that the higher error rate also allows a fixed response time, higher to bandwidth requirement, and in the judgement matrix of therefore table 2 and table 3, session service more values throughput situation.As can be seen from the figure, contrast other two kinds of methods, the inventive method can provide optimal jitter state, the bandwidth performance of gratifying packet loss and optimum, thus the qos requirement that can meet user.

Fig. 9-Figure 11 gives the performance of interactive service.Interaction service there are certain requirements the error rate, relatively low time delay and relatively high data downstream speed, and in the judgement matrix of therefore table 2 and table 3, session service more values packet loss situation.As can be seen from the figure, contrast other two kinds of methods, the inventive method can provide best packet loss performance and higher throughput under lowest costs prerequisite, thus can meet the qos requirement of user.

Figure 12-Figure 13 gives the performance of background business.Background business is very low to delay requirement, has higher bit error rate requirement, and in the judgement matrix of therefore table 2 and table 3, session service more values packet loss situation.As can be seen from the figure, contrast other two kinds of methods, the inventive method can provide best packet loss performance under lowest costs prerequisite, thus can meet the qos requirement of user.Therefore, according to above-mentioned simulation result and in conjunction with the feature of miscellaneous service type, can reach a conclusion, this method can provide according to type of service and make customer satisfaction system QoS.

The present invention uses Group Decision theory to combine weight vectors multiple in multiple attribute decision making (MADM), proposes a kind of network selecting method.When the method adopts Group Decision theory to obtain integrated network attribute weight, adopt four policymaker, quantity is moderate and representative respectively, comprehensive weight ensures its reasonability by united Lagrangian-Eulerian method inspection, namely the determination of weight is clear and definite and have abundant theory support, and correctly making full use of combinatorial theory, network selection procedures is unlikely to again undue complexity.Reached the impact considering network objective attribute, user preference and type of service by comprehensive multiple policymaker's wisdom in network selection procedures, can be made overall plans in the aspect that additive method cannot be taken into account, facts have proved, the method can according to the difference of type of service by given different judgement matrix for the user under different service types provides satisfied QoS.

Claims (1)

1., based on a heterogeneous network system of selection for four kinds of weight vectors, it is characterized in that the method comprises the following steps:
A. the foundation of network paramter matrix: the heterogeneous network model of this method comprises N kind heterogeneous network, the network attribute chosen comprises bandwidth (B), hardware circuit solution (R), packet delay (D), packet jitter (J), packet loss (L) and every bit expense (C);
Suppose that the network attribute matrix of the method is R=(x ij) n × 6, i.e. N number of heterogeneous network number, 6 kinds of network attributes, x ijrepresent a jth attribute of i-th network, and x i1represent the bandwidth of i-th network, x i2represent the hardware circuit solution of i-th network, x i3represent the time delay of i-th network, x i4represent the shake of i-th network, x i5represent the packet loss of i-th network, x i6represent every bit expense of i-th network, and 1<=i<=N, 1<=j<=6;
B. network attribute standard parameter: first network attribute matrix is carried out standardization, generally speaking, attribute is divided into profit evaluation model and cost type, and the attribute of profit evaluation model is the bigger the better: get the attribute of cost type is the smaller the better: get x min j = m i n ( x 1 j , x 2 j , ... , x N j ) ;
For bandwidth (B) and hardware circuit solution (R), they belong to profit evaluation model attribute, and standardization formula is: r i j = x i j x m a x j + x m i n j , 1 &le; j &le; 2 ;
For packet delay (D), packet jitter (J), packet loss (L) and every bit expense (C), they belong to cost type attribute, and standardization formula is: r i j = x m a x i + x m i n i - x i j x m a x j + x m i n j , 3 &le; j &le; 6 ;
After standardization, all properties parameter all will fall between zero and one, and the network paramter matrix after standardization is: R=(r ij) n × 6;
C. the determination of four groups of weight vectors: this standardized network attribute matrix is calculated two groups of objective weight vectors in conjunction with entropy assessment and dispersion method respectively, is respectively: with W &sigma; = W 2 = ( w 1 2 , w 2 2 , w 3 2 , w 4 2 , w 5 2 , w 6 2 ) ;
The judgment matrix table 2 corresponding according to different service types in given AHP method:
AHP method is utilized to calculate one group of subjective weight vectors in conjunction with network attribute matrix: same, the judgment matrix table 3 corresponding according to different service types in given G-1 method:
Type of service r 2 r 3 r 4 r 5 r 6 Sequence situation 2--> Session service 1.2 1.6 1 1.2 1.2 R>J>B>C>P>L Streaming media service 1.8 1 1 1 1 L>B>P>R>J>C Interaction service 1 1.8 1 1.4 1 L>C>R>J>B>P Background business 1.2 1.4 1 1 1.2 C>L>J>R>B>R
Wherein r k, 2≤k≤6 are the ratio of significance level between adjacent attribute under significance level sequence;
G-1 method is utilized to calculate another kind of subjective weight vectors: W G - 1 = W 4 = ( w 1 4 , w 2 4 , w 3 4 , w 4 4 , w 5 4 , w 6 4 ) ;
D. comprehensive weight vector and compatibility index is determined: establish A=(a ij), B=(b ij) and C=(c ij) being n rank positive reciprocal matrix, the ordering vector obtained by A is: W=(w 1, w 2..., w n) t, matrix W=(w i/ w j) be called the eigenmatrix of A; Product C (A, the B)=e of definition matrix A and matrix B t* A*B*e is the compatible degree of matrix A, B, wherein e t=(1,1 ... 1); Index S I (A, B)=C (A, B)/n 2be called matrix A, the compatibility index of B;
Usually, A and W has compatibility, but the compatibility of A and W is determined by A completely, and works as think that A and W has satisfied compatibility; For obtaining geometric generalization weight vectors, the four groups of weight vectors calculated are carried out Group Decision, in conjunction with formula:
w t = ( w t E W w t &sigma; w t A H P w t G - 1 ) / &Sigma; t = 1 6 ( w t E W w t &sigma; w t A H P w t G - 1 ) 1 / 4 = ( w t 1 w t 2 w t 3 w t 4 ) / &Sigma; t = 1 6 ( w t 1 w t 2 w t 3 w t 4 ) 1 / 4 Calculate geometric generalization average vector W and its eigenmatrix A; Four ordering vectors determined in step c are respectively W eW, W σ, W aHPand W g-1, can determine that their characteristic of correspondence matrixes are respectively A according to the definition of eigenmatrix eW, A σ, A aHPand A g-1, then determine that the compatibility index of these four eigenmatrixes and geometric generalization evaluation vector W is respectively according to the definition of compatibility index:
SI 1 = S I ( A , A E W ) = C ( A , A E W ) / 6 2 = &Sigma; i = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j E W / w i E W ) = &Sigma; i = 1 M ( w i / w i E W ) &Sigma; j = 1 M ( w j E W / w j ) / 6 2
SI 2 = S I ( A , A &sigma; ) = C ( A , A &sigma; ) / 6 2 = &Sigma; i = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j &sigma; / w i &sigma; ) = &Sigma; i = 1 M ( w i / w i &sigma; ) &Sigma; j = 1 M ( w j &sigma; / w j ) / 6 2
SI 3 = S I ( A , A G - 1 ) = C ( A , A G - 1 ) / 6 2 = &Sigma; i = 1 M &Sigma; j = 1 M ( w i / w j ) ( w j G - 1 / w i G - 1 ) = &Sigma; i = 1 M ( w i / w i G - 1 ) &Sigma; j = 1 M ( w j G - 1 / w j ) / 6 2
SI 4=SI (A, A aHP)=C (A, A aHP)/6 2, the eigenmatrix A of calculated characteristics matrix A and four kinds of weight vectors respectively eW, A σ, A aHPand A g-1compatibility index;
E. consistency check: for the ease of judging, get as the boundary value of compatibility index;
When whether compatiblely weighing A and B, when time the method think that A and B has gratifying compatibility; To judge whether these indexs are less than same order compatibility index critical value simultaneously in this step table 5:
If do not meet compatibility index, then need the subjectivity judgement matrix revised under this kind of type of service, turn back to step c; If met, network will be entered and select link;
F. network is selected: former step has calculated the geometric generalization weight vectors meeting compatibility index of six attributes, in this step, geometric generalization weight vectors calculates the cost function value of each network in conjunction with SAW method, to its sequence, get the maximum network of cost function value as objective network, thus obtain the optimum network numbering under this network state.
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