CN107608803A - A kind of enhanced social D2D relay selection methods towards multiple target - Google Patents
A kind of enhanced social D2D relay selection methods towards multiple target Download PDFInfo
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Abstract
The present invention provides a kind of enhanced social D2D relay selection methods towards multiple target, and this method is pre-processed to obtain the data of required type to input data；Obtained data are entered with the i.e. objective decision weight generation of row index to calculate；Conversion integration is carried out to the single goal output quantity and service quality QoS condition of acquisition, and then solved by distributed message pass through mechanism, obtains final output result.The combination of IFAHP method and entropy assessment of the algorithm based on modification, multi-objective optimization question is converted into single-object problem, and the further expression formula of Transformation Service quality requirements, and then D2D UE NW relay selection results are solved by suitable Message Passing Algorithm in a distributed manner.Subjective preferences of the VUE with hesitation degree and objective information decision weights are combined and accounted for by the present invention, so that relay selection result obtains more fully performance boost, possess higher equipment access ratio, handling capacity and system fairness.
Description
Technical field
The present invention relates to moving communicating field, is relayed more particularly, to a kind of enhanced social D2D towards multiple target
System of selection.
Background technology
The D2D communication technologys can to carry out direct communication between user terminal (UE), without by base station (eNB)
Etc. the transmission or forwarding of equipment, so as to reach the purpose for reducing eNB loads and expanding communication overlay.
UE-NW (UE-to-Network) relayings are the new spies that 3GPP LTE standard formulation groups introduce in D2D communicates subject under discussion
Property, there is the advantages of flexible deployment, network coverage, thus energy can be expanded in the case where not increasing conventional network equipment
It is widely used in business telecommunication, public safety communication (such as earthquake, war) field.As shown in figure 1, typical D2D
UE-NW systems include an eNB, some trunk subscriber equipment Relay UE (RUE) and some Victim UE (VUE, i.e.,
Need the UE of D2D relay connection services).ENB is connected with RUE by cellular communication link, and RUE and VUE then passes through 3GPP
The special secondary link of defined D2D communications is connected.VUE can be further divided into two types：In cellular network coverage
Out-of-Coverage VUE (OOC VUE) outside In-Coverage VUE (IC VUE), cellular network coverage.
Although D2D UE-NW trunking traffic technologies possess above-mentioned advantage, problem is still suffered from currently existing scheme, i.e., how
On the basis of conclusion as defined in existing 3GPP is met, D2D relay selection functions are efficiently performed.In this problem, a pass
The problem of key, is namely based on which kind of standard and method to perform the D2D relay selections under multiple-objection optimization.3GPP RAN2 working groups
In the conclusion that 2015 form, it is proposed that following to require：" VUE selection relaying UE, need to meet simultaneously link best in quality with
And the condition of other high-rise defineds ".
At the same time, as the development of social networks technology, D2D UE also show the social activity of holder more and more
Attribute.Social similarity between UE is higher, often also implies that higher trusting degree, namely the connection safety of higher degree
Property.In addition, high social similarity, which often also implies that VUE can have, gets sense in UE similar in bigger possibility from social activity
The data content of interest.Therefore, D2D social connection attribute obtains more and more extensive research attention rate.
However, there is different degrees of design defect in existing technical method.Typical concern physical link performance
Scheme, in the prior art, the maximum for updating D2D links in sub-clustering by iteration obtain reliable retransmission mechanism up to broadcast rate
The sub-clustering of lower minimum running time-frequency resource consumption and corresponding sub-clustering head.Connect however, the program ignores the social of D2D equipment presentation
Attribute is connect, and has too high computation complexity.Have in the prior art and propose in the social D2D with physical connection attribute of joint
After selection scheme, but such scheme be all assuming that social and physical connection attribute have identical processing priority weight under perform
, actual application scenarios might not be met.On the other hand, these methods are all the prioritization scheme under concern simple target,
The situation of multiple-objection optimization is not considered, and the access performance of UE-NW systems can be caused to lift more unification.
In addition to the scheme of above-mentioned single optimization aim, one kind is also proposed in the prior art and is based on complementary Fuzzy Level Analytic Approach
Method (Complementary Fuzzy Analytic Hierarchy Process, CFAHP) and mahalanobis distance
The multiple target D2D relay selection schemes that (Mahalanobis Distance, MD) is combined.However, more mesh in this scheme
Mark and do not include social attribute.In addition, the program depends on the subjective judgement that CFAHP is brought, have ignored objective data can provide
The favorable decisions information for relay selection.At the same time, the execution of CFAHP methods does not consider that UE subjectivities fuzzy Judgment is present
Hesitation degree psychology characteristic.On the other hand, this method fails to distinguish the different disposal strategy of income type and loss-type index.
The content of the invention
The present invention provides a kind of enhanced social D2D towards multiple target of the probability for the data for being lifted and obtaining target type
Relay selection method.
In order to reach above-mentioned technique effect, technical scheme is as follows：
A kind of enhanced social D2D relay selection methods towards multiple target, comprise the following steps：
S1：Input data is pre-processed to obtain the data of required type；
S2：Data in S1 are entered with the i.e. objective decision weight generation of row index to calculate；
S3：The single goal output quantity obtained in S2 and service quality QoS condition are subjected to conversion integration, and then pass through distribution
Formula message passing mechanism is solved, and obtains final output result.
Further, the detailed process of the step S1 is：
Make trunk subscriber equipment RUE composition set R={ R1,R2,...,RN, that N is candidate relay user equipment RUE
Number；The user equipment VUE composition set V={ v of relay connection service1,v2,...,vM, M is VUE number；
The index that this method needs to carry out decision weights generation calculating has：
Link capacity between VUE v and RUE r
Social similarity S between VUE v and RUE rv,r：By the German number description of outstanding person's card, it is defined as gathering around between VUE v and RUE r
The common social attribute having accounts for the ratio of total social attribute；
The cache size β at RUE r endsr；
VUE v obtain the consumption C needed for RUE r relay servicesv,r：In IC scenes, following the service in RUE execution is encouraged for VUE
Cost needed for business；And in OOC scenes, it is VUE itself power consumption；
Above-mentioned multiple target forms the goal set at VUE v endsWherein, capacity, social phase
It is gain-type index like degree and RUE ends cache size, numerical value is more high then more excellent；On the other hand, consumption is then loss-type index,
Numerical value is more low then more excellent；
At the same time, binary selection variable X is definedv,rTo indicate whether VUE v select candidate RUE r：
In addition to above-mentioned multiple target, the index for also needing to carry out decision weights generation calculating has：
VUE ends are to the QoS conditions required by each index；
The ability to accept at RUE ends, i.e., maximum accessible VUE quantity Kr；
Each VUE only has access only one RUE；
Based on the above, this method demand majorization of solutions model is as follows：
max{P1,P2,P3,-P4}(2)
The model is limited to：
Wherein：
Sv,thresh,βv,thresh,Cv,threshThe respectively capacity QoS threshold at VUE v ends, social similarity QoS thresholds
Value, QoS threshold is cached, consume QoS threshold.
Further, the detailed process of the step S2 includes carrying out the generation of subjective preferences decision weights and objective making decision
Weight generates；
The subjective preferences decision weights generation comprises the following steps：
1) fuzzy preference relation directly perceived, is constructed；
2) multiplying property of perfection unanimously fuzzy relation matrix directly perceived, is constructed；
3) Fuzzy Number Valued weight directly perceived, is generated；
4), generation determines number ordering values；
5) ordering values, generation output subjective preferences weight, are normalizedI.e. corresponding to i-th of index at VUE v ends
Subjective preferences weight；
The objective making decision weight generation comprises the following steps：
1) all candidate RUE indices numerical value, is inputted；
2), data prediction；
3) entropy assessment obtains objective making decision weight；
4) the single goal relative proximity that the result treatment generation that subjective preferences decision weights generate to obtain exports VUE v ends is integrated
Like degree series Τv。
Further, the detailed process of the step S3 is：
Pass through Τv, it is the minimum new optimization of solution single goal relative closeness RPD summations by former optimization problem model conversion
Model：
It is limited to：C1~C6 (6)
Because this Optimized model for including QoS threshold condition can not be asked directly by existing distributed message pass-algorithm
Solution, therefore, is further solving-optimizing problem (5), introduces following indicatrix：
Further, (7) are combined with the target of (4), obtain following transformation model：
It is limited to：
Wherein, C7 is to ensure (8) and (5) New Terms of equal value introduced in mathematical meaning, for model (8), (5)
Object function be combined with QoS threshold condition so that be equivalent to solve in the sense can be same for the solution of (8)
When meet minimum RPD and to meet the relay selection result of all QoS threshold conditions, due in practical communication system, condition C 7
It is difficult to be fully satisfied in binary select permeability under such QoS limitations, is carried out to be applicable distributed message pass through mechanism
Model solution, C7 is omitted, so as to solve the desired output result of (8), in order to be solved using distributed message pass through mechanism
(8), redefine in the t times iteration, the message at RUE ends to VUE ends is
And the message at VUE ends to RUE ends is
Wherein, ω is the predefined damped coefficient for ensureing algorithmic statement,Represent VUE composition set V
Delete the K in v-th of VUE residuary subset (i.e. V/ { v })rIndividual minimum message amount, and 0 ＜ Kr≤M,Kr∈Z+It is predefined
Parameter；
Because the solution target type of (8) is minimizes optimization summation, then correspondingly, the present invention sets distributed message to pass
The solution target of mechanism is passed to minimize size of message sum, obtains final iteration output：
Compared with prior art, the beneficial effect of technical solution of the present invention is：
The inventive method is pre-processed to obtain the data of required type to input data；Row index is entered to obtained data
That is objective decision weight generation calculates；Conversion integration is carried out to the single goal output quantity and service quality QoS condition of acquisition, and then
Solved by distributed message pass through mechanism, obtain final output result.IFAHP method of the algorithm based on modification
(Modified IFAHP, MIFAHP) and entropy assessment combination, single-object problem is converted to by multi-objective optimization question, and
The expression formula of further Transformation Service quality (Quality of Service, QoS) condition, and then pass through suitable message transmission
Algorithm solves D2D UE-NW relay selection results in a distributed manner.It is of the invention by subjective preferences of the VUE with hesitation degree and objective
Information decision weight, which is combined, to be accounted for, thus D2D UE-NW relay selections result can be caused to obtain more fully performance
Lifting.At the same time, the present invention is compared to existing program, possesses higher equipment access ratio, higher handling capacity, higher
System fairness, thus it is more suitable for actual D2D UE-NW communication systems.
Brief description of the drawings
Fig. 1 is typical D2D UE-NW systems；
Fig. 2 is basic procedure of the present invention；
Fig. 3 is IFAHP methods (Modified IFAHP, MIFAHP) flow chart based on modification；
Fig. 4 is the target weight generating algorithm flow chart of joint IFAHP methods and entropy assessment；
Fig. 5 was the flow chart that distributed message passing mechanism is solved；
Fig. 6 is that IC scenes access ratio vs capacity QoS thresholds；
Fig. 7 is that IC scenes access ratio vs social activity similarity QoS thresholds；
Fig. 8 is that IC scenes access ratio vs consumption QoS thresholds；
Fig. 9 is that IC scenes access ratio vs cache size QoS thresholds；
Figure 10 is that IC scenes access the full QoS thresholds of ratio vs；
Figure 11 is that OOC scenes access ratio vs capacity QoS thresholds；
Figure 12 is that OOC scenes access ratio vs social activity similarity QoS thresholds；
Figure 13 is that OOC scenes access ratio vs consumption QoS thresholds；
Figure 14 is that OOC scenes access ratio vs caching QoS thresholds；
Figure 15 is that OOC scenes access the full QoS thresholds of ratio vs；
Figure 16 is the full QoS thresholds of IC scene handling capacity vs；
Figure 17 is the full QoS thresholds of IC scene system fairness vs；
Figure 18 is the full QoS thresholds of OOC scene handling capacity vs；
Figure 19 is the full QoS thresholds of OOC scene system fairness vs.
Embodiment
Accompanying drawing being given for example only property explanation, it is impossible to be interpreted as the limitation to this patent；
In order to more preferably illustrate the present embodiment, some parts of accompanying drawing have omission, zoomed in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be appreciated that some known features and its explanation, which may be omitted, in accompanying drawing
's.
Technical scheme is described further with reference to the accompanying drawings and examples.
Embodiment 1
A kind of enhanced social D2D relay selection methods towards multiple target, comprise the following steps：
S1：Input data is pre-processed to obtain the data of required type；
S2：Data in S1 are entered with the i.e. objective decision weight generation of row index to calculate；
S3：The single goal output quantity obtained in S2 and service quality QoS condition are subjected to conversion integration, and then pass through distribution
Formula message passing mechanism is solved, and obtains final output result.
Further, the detailed process of the step S1 is：
Make trunk subscriber equipment RUE composition set R={ R1,R2,...,RN, that N is candidate relay user equipment RUE
Number；The user equipment VUE composition set V={ v of relay connection service1,v2,...,vM, M is VUE number；
The index that this method needs to carry out decision weights generation calculating has：
Link capacity between VUE v and RUE r
Social similarity S between VUE v and RUE rv,r：By the German number description of outstanding person's card, it is defined as gathering around between VUE v and RUE r
The common social attribute having accounts for the ratio of total social attribute；
The cache size β at RUE r endsr；
VUE v obtain the consumption C needed for RUE r relay servicesv,r：In IC scenes, following the service in RUE execution is encouraged for VUE
Cost needed for business；And in OOC scenes, it is VUE itself power consumption；
Above-mentioned multiple target forms the goal set at VUE v endsWherein, capacity, social phase
It is gain-type index like degree and RUE ends cache size, numerical value is more high then more excellent；On the other hand, consumption is then loss-type index,
Numerical value is more low then more excellent；
At the same time, binary selection variable X is definedv,rTo indicate whether VUE v select candidate RUE r：
In addition to above-mentioned multiple target, the index for also needing to carry out decision weights generation calculating has：
VUE ends are to the QoS conditions required by each index；
The ability to accept at RUE ends, i.e., maximum accessible VUE quantity Kr；
Each VUE only has access only one RUE；
Based on the above, this method demand majorization of solutions model is as follows：
max{P1,P2,P3,-P4} (2)
The model is limited to：
Wherein：
Sv,thresh,βv,thresh,Cv,threshThe respectively capacity QoS threshold at VUE v ends, social similarity QoS thresholds
Value, QoS threshold is cached, consume QoS threshold.
Further, the detailed process of the step S2 includes carrying out the generation of subjective preferences decision weights and objective making decision
Weight generates；
The subjective preferences decision weights generation comprises the following steps：
1) fuzzy preference relation directly perceived, is constructed；
2) multiplying property of perfection unanimously fuzzy relation matrix directly perceived, is constructed；
3) Fuzzy Number Valued weight directly perceived, is generated；
4), generation determines number ordering values；
5) ordering values, generation output subjective preferences weight, are normalizedI.e. corresponding to i-th of index at VUE v ends
Subjective preferences weight；
The objective making decision weight generation comprises the following steps：
1) all candidate RUE indices numerical value, is inputted；
2), data prediction；
3) entropy assessment obtains objective making decision weight；
4) the single goal relative proximity that the result treatment generation that subjective preferences decision weights generate to obtain exports VUE v ends is integrated
Like degree series Τv。
Further, the detailed process of the step S3 is：
Pass through Τv, it is the minimum new optimization of solution single goal relative closeness RPD summations by former optimization problem model conversion
Model：
It is limited to：C1~C6 (6)
Because this Optimized model for including QoS threshold condition can not be asked directly by existing distributed message pass-algorithm
Solution, therefore, is further solving-optimizing problem (5), introduces following indicatrix：
Further, (7) are combined with the target of (4), obtain following transformation model：
It is limited to：
Wherein, C7 is to ensure (8) and (5) New Terms of equal value introduced in mathematical meaning, for model (8), (5)
Object function be combined with QoS threshold condition so that be equivalent to solve in the sense can be same for the solution of (8)
When meet minimum RPD and to meet the relay selection result of all QoS threshold conditions, due in practical communication system, condition C 7
It is difficult to be fully satisfied in binary select permeability under such QoS limitations, is carried out to be applicable distributed message pass through mechanism
Model solution, C7 is omitted, so as to solve the desired output result of (8), in order to be solved using distributed message pass through mechanism
(8), redefine in the t times iteration, the message at RUE ends to VUE ends is
And the message at VUE ends to RUE ends is
Wherein, ω is the predefined damped coefficient for ensureing algorithmic statement,Represent VUE composition set V
Delete the K in v-th of VUE residuary subset (i.e. V/ { v })rIndividual minimum message amount, and 0 ＜ Kr≤M,Kr∈Z+It is predefined
Parameter；
Because the solution target type of (8) is minimizes optimization summation, then correspondingly, the present invention sets distributed message to pass
The solution target of mechanism is passed to minimize size of message sum, obtains final iteration output：
In order to more fully illustrate beneficial effect possessed by the present invention, below in conjunction with specific embodiment and related emulation
As a result and analyze, further effectiveness of the invention and advance are explained.
Assuming that system is random equally distributed by eNB, a N=10 random equally distributed D2D RUE and M=30
VUE is formed.Wherein, the maximum accessible VUE quantity K in each RUE endsrNumerical value uniformly random generation in the range of [4,6].
In practical application, KrConcrete numerical value can be by RUE according to own situation (dump energy, security, shared wish of user etc.
Factor) determine, and report the numerical value to eNB.VUE then according to whether covering, is further divided into IC VUE and OOC VUE.D2D chains
Channel is lost to describe in route large scale, wherein, path loss is dominated by the distance between D2D links.In order to which the facility of statement, coordinate are horizontal
Axis values 0.5 represent the average of all candidate RUE index QoS numerical value, then have the average of 0.4 expression 80% successively, and 0.6 represents
120% average.The present embodiment uses the true social network experiment data of CRAWDAD upb/hyccups (v.2016-10-17)
To simulate social index of similarity numerical value.
USARA schemes of the present invention will be compared with foregoing typical D2D UE-NW relay selection schemes, i.e.,：Maximum thing
Manage maximum social similarity (the Max Physical Max of link capacity type (Max Physical), greatest physical link capacity
Social, MPMS), hybrid selection (Hybrid Selection Scheme, HRS) and based on complementary Fuzzy Level Analytic Approach-
Mahalanobis distance method (FAHP-M).Wherein, to realize comparability, in emulation testing, the minimal physical in MPMS methods and HRS methods
Distance objective conversion of equal value is for maximum capacity target.
In system input, set typical subjective fuzzy decision sequence (from high to low) as follows：
■ IC scenes：Capacity → social similarity → consumption → cache size；
■ OOC scenes：Capacity → consumption → cache size → social similarity.
As described above, in IC scenes, " consumption " is defined as the cost needed for VUE excitation RUE execution relay services；
In OOC scenes, " consumption " is defined as VUE itself power consumption.
For MIFAHP methods proposed by the present invention and FAHP-M methods, its execution needs to input predefined typical case's mould directly perceived
Paste input matrix and complementary fuzzy input matrix.An example of these matrixes is given in Tables 1 and 2.In systems in practice,
The value of each element should be chosen according to the situation of network in matrix.
Typical case's Indistinct Input matrix directly perceived needed for the MIFAHP methods of table 1
(a) IC scenes
| Index | Capacity | Social similarity | Consumption | Cache size |
| Capacity | (0.5,0.5) | (0.6,0.2) | (0.7,0.1) | (0.8,0.1) |
| Social similarity | (0.2,0.6) | (0.5,0.5) | (0.6,0.2) | (0.7,0.1) |
| Consumption | (0.1,0.7) | (0.2,0.6) | (0.5,0.5) | (0.6,0.2) |
| Cache size | (0.1,0.8) | (0.1,0.7) | (0.2,0.6) | (0.5,0.5) |
(b) OOC scenes
| Index | Capacity | Social similarity | Consumption | Cache size |
| Capacity | (0.5,0.5) | (0.9,0.1) | (0.6,0.2) | (0.7,0.1) |
| Social similarity | (0.1,0.9) | (0.5,0.5) | (0.1,0.7) | (0.2,0.6) |
| Consumption | (0.2,0.6) | (0.7,0.1) | (0.5,0.5) | (0.7,0.1) |
| Cache size | (0.1,0.7) | (0.6,0.2) | (0.1,0.7) | (0.5,0.5) |
Exemplary complementary Indistinct Input matrix needed for the FAHP-M methods of table 2
(a) IC scenes
| Index | Capacity | Social similarity | Consumption | Cache size |
| Capacity | 0.5 | 0.6 | 0.7 | 0.8 |
| Social similarity | 0.4 | 0.5 | 0.6 | 0.7 |
| Consumption | 0.3 | 0.4 | 0.5 | 0.6 |
| Cache size | 0.2 | 0.3 | 0.4 | 0.5 |
(b) OOC scenes
| Index | Capacity | Social similarity | Consumption | Cache size |
| Capacity | 0.5 | 0.9 | 0.6 | 0.7 |
| Social similarity | 0.1 | 0.5 | 0.1 | 0.2 |
| Consumption | 0.4 | 0.9 | 0.5 | 0.7 |
| Cache size | 0.3 | 0.8 | 0.3 | 0.5 |
In Fig. 6-Fig. 9 and Figure 11-Figure 14, to investigate a kind of index of classification, become using its QoS threshold as independent variable
Change, and the value of other indexs is then fixed on 0.5；In Fig. 9 and Figure 14, the QoS threshold (All of all types index
Thresholds) all it is changed as independent variable.When QoS threshold reduces, VUE access threshold reduces, thus all
The access ratio of scheme can all improve.When QoS threshold is minimum, the access ratio of each scheme reaches maximum.Pass through Fig. 5-figure
14 understand that scheme (the Proposed curves in i.e. each figure) proposed by the present invention can obtain the access higher than other all schemes
Ratio.This is due to this programme while takes minimum RPD and QoS indicatrix φ, so that system can be obtained as much as possible
The preferable weight values of all indexs are obtained, and meet the QoS conditions of all indexs as far as possible.By contrast, it is other existing
Scheme can not meet above-mentioned two purpose simultaneously.
The present invention is tested to system throughput and fairness.It is proposed by the present invention by Figure 16-Figure 19
USARA schemes can obtain higher system throughput and fairness than existing scheme.Wherein, the advantage of throughput is directly benefited
In the high access ratio performance of this programme.In addition, the optimization process of USARA schemes considers the QoS conditions of all types index,
Rather than single or some types index QoS conditions are focused simply on, so that system obtains higher fairness.
Especially, this programme in addition to reference to VUE subjective preferences weights, also by means of index for FAHP-M
The objective making decision weight that data provide carries out more comprehensive RUE relay selections and judged.In addition, this programme is pre- in data normalization
Processing stage, different processing modes is taken to gain-type index and loss-type index, to embody the inclined of different type index
Good trend.
Same or analogous label corresponds to same or analogous part；
Position relationship is used for being given for example only property explanation described in accompanying drawing, it is impossible to is interpreted as the limitation to this patent；
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair
The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description
To make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (4)
1. a kind of enhanced social D2D relay selection methods towards multiple target, it is characterised in that comprise the following steps：
S1：Input data is pre-processed to obtain the data of required type；
S2：Data in S1 are entered with the i.e. objective decision weight generation of row index to calculate；
S3：The single goal output quantity obtained in S2 and service quality QoS condition are subjected to conversion integration, and then disappeared by distribution
Breath pass through mechanism is solved, and obtains final output result.
2. the enhanced social D2D relay selection methods according to claim 1 towards multiple target, it is characterised in that institute
Stating step S1 detailed process is：
Make trunk subscriber equipment RUE composition set R={ R1,R2,...,RN, N is candidate relay user equipment RUE number；In
After the user equipment VUE composition set V={ v of Connection Service1,v2,...,vM, M is VUE number；
The index that this method needs to carry out decision weights generation calculating has：
Link capacity between VUE v and RUE r
Social similarity S between VUE v and RUE rv,r：By the German number description of outstanding person's card, it is defined as what is possessed between VUE v and RUE r
Common social attribute accounts for the ratio of total social attribute；
The cache size β at RUE r endsr；
VUE v obtain the consumption C needed for RUE r relay servicesv,r：In IC scenes, RUE is encouraged to perform relay services institute for VUE
The cost needed；And in OOC scenes, it is VUE itself power consumption；
Above-mentioned multiple target forms the goal set at VUE v endsWherein, capacity, social similarity with
And RUE ends cache size is gain-type index, numerical value is more high then more excellent；On the other hand, consumption is then loss-type index, and numerical value is got over
It is low then more excellent；
At the same time, binary selection variable X is definedv,rTo indicate whether VUE v select candidate RUE r：
In addition to above-mentioned multiple target, the index for also needing to carry out decision weights generation calculating has：
VUE ends are to the QoS conditions required by each index；
The ability to accept at RUE ends, i.e., maximum accessible VUE quantity Kr；
Each VUE only has access only one RUE；
Based on the above, this method demand majorization of solutions model is as follows：
max{P1,P2,P3,-P4} (2)
The model is limited to：
Wherein：
Sv,thresh,βv,thresh,Cv,threshThe respectively capacity QoS threshold at VUE v ends, social similarity QoS threshold, delay
QoS threshold is deposited, consumes QoS threshold.
3. the enhanced social D2D relay selection methods according to claim 2 towards multiple target, it is characterised in that institute
Stating step S2 detailed process includes carrying out the generation of subjective preferences decision weights and the generation of objective making decision weight；
The subjective preferences decision weights generation comprises the following steps：
1) fuzzy preference relation directly perceived, is constructed；
2) multiplying property of perfection unanimously fuzzy relation matrix directly perceived, is constructed；
3) Fuzzy Number Valued weight directly perceived, is generated；
4), generation determines number ordering values；
5) ordering values, generation output subjective preferences weight, are normalizedMaster i.e. corresponding to i-th of index at VUE v ends
See preference weight；
The objective making decision weight generation comprises the following steps：
1) all candidate RUE indices numerical value, is inputted；
2), data prediction；
3) entropy assessment obtains objective making decision weight；
4) the single goal relative closeness that the result treatment generation that subjective preferences decision weights generate to obtain exports VUE v ends is integrated
Sequence Τv。
4. the enhanced social D2D relay selection methods according to claim 3 towards multiple target, it is characterised in that institute
Stating step S3 detailed process is：
Pass through Τv, it is the minimum new Optimized model of solution single goal relative closeness RPD summations by former optimization problem model conversion：
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<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
It is limited to：C1~C6 (6)
Because this Optimized model for including QoS threshold condition can not be solved directly by existing distributed message pass-algorithm, because
This, is further solving-optimizing problem (5), introduces following indicatrix：
Further, (7) are combined with the target of (4), obtain following transformation model：
It is limited to：
Wherein, C7 is to ensure (8) and (5) New Terms of equal value introduced in mathematical meaning, for model (8), the mesh of (5)
Scalar functions are combined with QoS threshold condition, can be expired simultaneously so that the solution of (8) is equivalent to solve in the sense
The minimum RPD of foot and the relay selection result for meeting all QoS threshold conditions, because in practical communication system, condition C 7 is at this
It is difficult to be fully satisfied in binary select permeability under class QoS limitations, model is carried out in order to be applicable distributed message pass through mechanism
Solve, omit C7, so as to solve the desired output result of (8), in order to solve (8) using distributed message pass through mechanism, weight
Define in the t times iteration, the message at RUE ends to VUE ends is
<mrow>
<msubsup>
<mi>&alpha;</mi>
<mrow>
<mi>v</mi>
<mo>,</mo>
<mi>r</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>=</mo>
<mo>-</mo>
<mi>&omega;</mi>
<mo><</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>,</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<msubsup>
<mo>></mo>
<mi>v</mi>
<msub>
<mi>K</mi>
<mi>r</mi>
</msub>
</msubsup>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<mi>&omega;</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>v</mi>
</mrow>
<mi>t</mi>
</msubsup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
And the message at VUE ends to RUE ends is
Wherein, ω is the predefined damped coefficient for ensureing algorithmic statement,Represent that VUE composition set V is deleted
K in v-th of VUE residuary subset (i.e. V/ { v })rIndividual minimum message amount, and 0 ＜ Kr≤M,Kr∈Z+For predefined ginseng
Number；
Because the solution target type of (8) is minimizes optimization summation, then correspondingly, the present invention sets distributed message to transmit machine
The solution target of system obtains final iteration output to minimize size of message sum：
<mrow>
<msup>
<mi>r</mi>
<mo>*</mo>
</msup>
<mo>=</mo>
<munder>
<mrow>
<mi>arg</mi>
<mi> </mi>
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<mi>i</mi>
<mi>n</mi>
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<mi>r</mi>
</munder>
<mrow>
<mo>(</mo>
<msub>
<mi>a</mi>
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<mo>,</mo>
<mi>v</mi>
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</msub>
<mo>+</mo>
<msub>
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<mrow>
<mi>r</mi>
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</mrow>
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<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
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