CN110418288A - D2D multi-casting communication cluster-dividing method based on user social contact attribute - Google Patents
D2D multi-casting communication cluster-dividing method based on user social contact attribute Download PDFInfo
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Abstract
The invention discloses a kind of D2D multicast cluster-dividing method based on user social contact attribute solves the problems, such as the poor bring user performance loss of existing D2D multicast cluster stability.Method includes the following steps: S101, randomly selecting K D2D user and assigning in K cluster；S102, the mean vector m for calculating each clusterk；S103, to nth user, calculate separately it arrive each cluster sub-clustering Factor distance；S104, user n is divided into the nearest cluster of sub-clustering Factor distance；S105, the new mean vector m of k-th cluster (k=1 ..., K) is calculatedk′；S106 judges k-th of brand new mean vector mk' mean vector the m with current clusterkIt is whether equal, if unequal, the mean vector newly calculated is replaced into former mean vector；If equal, without modification；S107 is returned and is executed S103-S106, until the result that the mean vector and previous round of each cluster calculate is equal, algorithmic statement at this time；S108, it is when by algorithmic statement as a result, as final sub-clustering result.
Description
[technical field]
The invention belongs to fields of communication technology, and in particular to a kind of D2D multi-casting communication sub-clustering based on user social contact attribute
Method.
[background technique]
Huge data traffic demand, the type of business to become increasingly abundant cause wireless communication network load increasingly heavier.Think
Section's statistics in 2018 and prediction show from 2017 to 2022 year, and wireless mobile apparatus data volume will increase by 7 times, 2022
Year mobile data flow will reach 77.5Exabytes monthly.Mobile data flow will be between 2017 to 2022
46% compound annual growth rate (Compound Annual Growth Rate, CAGR) sustainable growth.From this, following move
The demand of dynamic data traffic will persistently explode, this brings huge challenge to the load capacity of mobile radio network.
As one of the key technology of the 5th generation (5th-Generation, 5G) mobile communication, the D2D communication technology is being promoted
QoS of customer (Quality of Service, QoS), the covering of extension cellular system and raising system performance etc. have
Wide application prospect becomes the research hotspot of current industry circle and academia.D2D multicast communication technologies can complete a use
Family sends data to multiple users simultaneously and greatly improves the availability of frequency spectrum by user sharing frequency spectrum resource.With more matchmakers
The commercialization of body business sharply increased with 5G communication, D2D multi-casting communication using more and more extensive, for D2D multi-casting communication into
Row system research is very necessary.
Terminal device is imparted social attribute due to artificially carrying indirectly.By the communication attributes of terminal device and user
Between social attribute combine, construct user between direct communication network, can more efficiently realize resource-sharing, further increase
The validity and reliability of transmission.
However, less social attribute, the mobility for considering user in existing D2D cluster-based techniques, hypothesis user are in standard
Static state causes the performance of D2D communication user that can not ensure so that obtained D2D multicast cluster stability is poor.At present
In most of documents, user social contact attribute factor only indicates whether user has social networks by binary variable, with upper mold
Type is difficult to accurately measure the social networks between user.As it can be seen that at present more to the modeling method of the social attribute of D2D communication user
It is single, need to user social contact attribute carry out deeper into analysis and more reasonably modeling.So as to more efficiently realize money
Source is shared, further increases the validity and reliability of transmission.
[summary of the invention]
The object of the present invention is to provide a kind of D2D multicast cluster-dividing method based on user social contact attribute, to solve existing D2D
The problem of poor bring user performance of multicast cluster stability is lost.
The invention adopts the following technical scheme: the D2D multicast cluster-dividing method based on user social contact attribute, including following step
It is rapid:
S101, K D2D user { u is randomly selected1,…,un,…,uKAssign to { C in K cluster1,…,Ck,…CK}；un(n
=1 ..., K) indicate that n-th of D2D communication user, K are the total number of clusters of D2D multi-casting communication, Ck(k=1 ..., K) it indicates k-th
D2D multi-casting communication cluster；
S102, the mean vector for calculating each cluster:
Wherein, mkIndicate the mean vector of k-th of cluster, xnIndicate the coordinate of n-th of D2D communication user, ckFor k-th of cluster
The coordinate of central user, | | xn-ck||2Indicate n-th of D2D communication user to k-th of cluster central point Euclidean distance,For
For nth user to the social attribute factor of k-th of cluster central user, ζ and η are proportionality coefficient, meet+η=1 ζ, | Ck| indicate the
D2D number of users in k cluster；
S103, to nth user, calculate separately it arrive each cluster sub-clustering Factor distance:
dk,nIndicate n-th of D2D communication user to k-th of cluster central user sub-clustering Factor distance, choose the sub-clustering factor away from
Cluster from nearest cluster as nth user's final choice, i.e.,
Wherein, C* nFor the cluster number of nth user's selection；
S104, user n is divided into cluster C* nIn；
S105, to k-th of cluster (k=1 ..., K), calculate new mean vector mk′；
S106 judges the new mean vector m of k-th of cluster (k=1 ..., K)k' mean vector the m with current clusterkWhether phase
Deng；If unequal mk≠mk', the mean vector newly calculated is replaced former mean vector by (k=1 ..., K)If equal mk=mk', (k=1 ..., K), the value of former mean vector is without modification；
S107 is returned and is executed S103-S106, until the mean vector that the mean vector and previous round of each cluster calculate
It is equal, algorithmic statement at this time；
S108, it is when by algorithmic statement as a result, as final sub-clustering result.
Further, after obtaining final sub-clustering result, in k-th of cluster (k=1 ..., K), the choosing of core customer is carried out
It selects, method particularly includes: by core customer's fitness F of nth usernAs measurement user if appropriate for as core customer
Parameter, indicate are as follows:
Wherein, α, β,τ is weight parameter, is metEnFor the available battery capacity of nth user,
DnFor the social factor of nth user, PnFor the resident probability of nth user, SnFor the free memory of nth user；
According to the fitness of potential core customer height, select in k-th of cluster the highest user of fitness as k-th of cluster
Core node, transmission node of the core node as D2D multi-casting communication；Wherein, nkIt * is kth
Core customer in a cluster；FnFor core customer's fitness of user n, ΩkIndicate the set of all D2D users in k-th of cluster.
Further, the social factor D of nth usernIt is calculate by the following formula:
Wherein,For nth user to the social attribute factor between m-th of user, m ∈ Ωk, m-th of m ≠ n expression
User is the user in k-th of cluster, meanwhile, m-th of user and nth user's difference.
Further, the resident probability P of nth usern=Tc,n/Ts,n, wherein Ts,nIndicate nth user in all clusters
Interior resident total historical time, Tc,nIndicate the time that nth user is resident in current cluster.
Further, nth user may be expressed as: to the social attribute factor between m-th of userWherein, ρ is weight factor,It is average between nth user and m-th of user
It is associated with the normalization expression of duration,For nth user and m-th of user D2D communications income both account for it is total
Transmit the ratio of income.
Further, the normalization that duration is averagely associated between nth user and m-th of user indicates:WhereinIt indicates within the Δ t time, content share is flat between nth user and m-th of user
Equal duration, specific formula for calculation are as follows:
Wherein, zn,m(t) the content share state between t moment nth user and m-th of user, z are indicatedn,m(t)=1
Show between t moment nth user and m-th of user in content share state；Otherwise zn,m(t)=0；δn,m(t) when indicating t
Carve the content share value between nth user and m-th of user.
Further, within the Δ t time, nth user accounts for therebetween the D2D communications income of m-th of user
The ratio of total transmission incomeIt can be calculate by the following formula:
Wherein, Cotn,m(t) transmission income of the t moment nth user to m-th of user, Cot are indicatedm,n(t) when indicating t
M-th of user is carved to the transmission income of nth user.
The beneficial effects of the present invention are: user social contact attribute factor is introduced D2D multi-casting communication cluster algorithm, energy by the present invention
More stable D2D multi-casting communication transmission group is enough established, more efficiently realization resource-sharing further increases D2D communications
Validity and reliability.
[Detailed description of the invention]
Fig. 1 is the system utility figure of the embodiment of the present invention；
Fig. 2 is the communication throughput spirogram of the embodiment of the present invention；
Fig. 3 is the outage probability figure of the embodiment of the present invention；
Fig. 4 is the cluster robustness figure of the embodiment of the present invention；
Fig. 5 is that the present invention is based on the method flow diagrams of the D2D multi-casting communication cluster-dividing method of user social contact attribute.
[specific embodiment]
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
One, the present invention provides a kind of D2D multicast cluster-dividing method based on user social contact attribute.
1, this method is based on a D2D communication user social attribute model, and model is specific as follows:
Frequency that social attribute between user is contacted by user, connection duration, user's cohesion, interaction mode shadow
It rings.Since the research object of this project is the data transmission between D2D communication user, present invention primarily contemplates between user
Carry out the frequency and duration of content share.The social degree of association passes through in content between two users in a determining duration between user
The duration of the number of sharing and each content share describes.
Enable δn,m(t) the content share value between t moment nth user and m-th of user, z are indicatedn,m(t) t moment is indicated
Content share state between nth user and m-th of user, zn,m(t)=1 show t moment nth user and m-th of user
Between be in content share state；Otherwise zn,m(t)=0.The content point between nth user and m-th of user within the Δ t time
The mean time enjoyed is long are as follows:
According to Gauss similarity function, the normalization that duration is averagely associated between available user is indicated:
In the case where D2D communicates incentive mechanism, within the Δ t time, D2D communications income of the nth user to m-th of user
Account for the ratio of transmission income total therebetween are as follows:
Wherein Cotn,m(t) transmission income of the t moment nth user to m-th of user, Cot are indicatedm,n(t) t moment is indicated
Transmission income of m-th of user to nth user.
Incidence relation belongs to income, nth user is transmitted to the social activity between m-th of user between comprehensively considering D2D user
Sex factor may be expressed as:
Wherein, ρ is weight factor, can be adjusted according to business characteristic.
2, for stable and firm D2D multicast group, in sub-clustering, it is contemplated that the social networks between user.Based on society
The K mean value cluster algorithm of friendship is in order to solve following optimization problem:
Wherein, | | xj-ci||2Refer to the Euclidean distance between user j and i-th of cluster central point.ωijFor distribution parameter, when
(x when user j belongs to i-th of clusterj∈Ci), ωij=1, otherwise, ωij=0.C2 indicates that each cluster is not empty；C3 indicates each use
Family can only exist in a cluster.Problem above is NP-hard problem, even if K=2.To solve the above-mentioned problems, the present invention proposes
A kind of K mean value cluster algorithm based on social activity.
It is in order to by N number of D2D user { u based on social K mean value cluster algorithm1,u2,…,uNBe divided into K cluster
{C1,C2,…CK, as shown in figure 5, basic step is as follows:
S101, K D2D user { u is randomly selected1,…,un,…,uKAssign to { C in K cluster1,…,Ck,…CK}；un(n
=1 ..., K) indicate that n-th of D2D communication user, K are the total number of clusters of D2D multi-casting communication, Ck(k=1 ..., K) it indicates k-th
D2D multi-casting communication cluster.
S102, the mean vector for calculating each cluster:
Wherein, mkIndicate the mean vector of k-th of cluster, xnIndicate the coordinate of n-th of D2D communication user, ckFor k-th of cluster
The coordinate of central user, | | xn-ck||2Indicate n-th of D2D communication user to k-th of cluster central point Euclidean distance,For
For nth user to the social attribute factor of k-th of cluster central user, ζ and η are proportionality coefficient, meet+η=1 ζ, | Ck| indicate the
D2D number of users in k cluster.
S103, to nth user, calculate separately it arrive each cluster sub-clustering Factor distance:
dk,nIndicate n-th of D2D communication user to k-th of cluster central user sub-clustering Factor distance, choose the sub-clustering factor away from
Cluster from nearest cluster as nth user's final choice, i.e.,
Wherein, C*nFor the cluster number of nth user's selection；
S104, user n is divided into cluster C*nIn；
S105, to k-th of cluster (k=1 ..., K), calculate new mean vector mk′；
S106, judge the new mean vector m of k-th of cluster (k=1 ..., K)k' mean vector the m with current clusterkWhether phase
Deng；If unequal mk≠mk', the mean vector newly calculated is replaced former mean vector by (k=1 ..., K)If equal mk=mk', (k=1 ..., K), the value of former mean vector is without modification；
S107, execution S103-S106 is returned to, until the mean vector that the mean vector and previous round of each cluster calculate
It is equal, algorithmic statement at this time；
S108, by algorithmic statement when as a result, as final sub-clustering result.
3, it after completing D2D multicast sub-clustering, needs that core customer is selected to click through row data as D2D emitter junction in cluster to pass
It is defeated, specific as follows:
In order to meet message transmission rate demand, select to comprehensively consider the electricity of user terminal when D2D core customer, deposit
Store up space and mobility.
The user in a cluster is resided in for a long time, also has more high probability resident in transmission process, if as core
User has higher reliability.The resident probability P of usernResidence time is related in cluster with user.Enable resident probability Pn=
Tc,n/Ts,n, Ts,nIndicate total historical time that nth user is resident in all clusters, Tc,nIndicate nth user in current cluster
The resident time.
By core customer's fitness F of nth usernAs measuring user if appropriate for the parameter as core customer,
It indicates are as follows:
Wherein, α, β,τ is weight parameter, is metEnFor the available battery capacity of nth user,
DnFor the social factor of nth user, PnFor the resident probability of nth user, SnFor the free memory of nth user；
According to the fitness of potential core customer height, select in k-th of cluster the highest user of fitness as k-th of cluster
Core node, transmission node of the core node as D2D multi-casting communication；Wherein, nkIt * is k-th
Core customer in cluster；FnFor core customer's fitness of user n, ΩkIndicate the set of all D2D users in k-th of cluster.
Wherein, the social factor D of nth usernIt is calculate by the following formula:
Wherein,For nth user to the social attribute factor between m-th of user, m ∈ Ωk, m-th of m ≠ n expression
User is the user in k-th of cluster, meanwhile, m-th of user and nth user's difference.
The resident probability P of nth usern=Tc,n/Ts,n, wherein Ts,nIndicate what nth user was resident in all clusters
Total historical time, Tc,nIndicate the time that nth user is resident in current cluster.
Nth user may be expressed as: to the social attribute factor between m-th of user
Wherein, ρ is weight factor,The normalization table of duration is averagely associated between nth user and m-th of user
Show,The ratio of the total transmission income of the two is accounted for for the D2D communications income of nth user and m-th of user.
The normalization that duration is averagely associated between nth user and m-th of user indicates:Its
InIndicate the average duration of content share, specific formula for calculation between nth user and m-th of user within the Δ t time
It is as follows:
Wherein, zn,m(t) the content share state between t moment nth user and m-th of user, z are indicatedn,m(t)=1
Show between t moment nth user and m-th of user in content share state；Otherwise zn,m(t)=0；δn,m(t) when indicating t
Carve the content share value between nth user and m-th of user.
Within the Δ t time, nth user accounts for transmission total therebetween to the D2D communications income of m-th of user and receives
The ratio of benefitIt can be calculate by the following formula:
Wherein, Cotn,m(t) transmission income of the t moment nth user to m-th of user, Cot are indicatedm,n(t) when indicating t
M-th of user is carved to the transmission income of nth user.
Two, system utility Functional Analysis:
Enable set Φ={ 1,2 ..., M } that Φ is D2D communication core user, m ∈ Φ.Ψ is the set of receiving node,
Ψ={ 1,2 ..., U }, user u ∈ Ψ.Enable all available subcarrier set in Θ={ 1 ..., K } expression system.According to perfume (or spice)
Agriculture formula can derive that m-th of core customer connects u-th and receive the transmission rate of user on sub-carrierk are as follows:
Wherein, B is unit bandwidth, N0For the noise under unit bandwidth, NmIndicate the reception user collection of m-th of D2D multicast group
It closes,Channel gain of the transmitting node m to receiving node u ' on sub-carrierk is indicated, further, it is possible to acquire average transmission
Rate:
Wherein, T is transmission time.
Enable αuIndicate the income of user u per unit data transfer rate, βm,uFor the bandwidth consumed when user u connection sending node m,
γm,uThe bandwidth that user u is consumed when feeding back to sending node m, φuIndicate that user u selection D2D communicates bring per unit data
The income of rate, ψuFor the cost of user's u unit memory space.Indicate storing data ruThe memory space of occupancy, BeuExpression is estimated
The channel width that meter feedback occupies.Utility function between user u and sending node m (m ≠ 0) are as follows:
Wherein, am,uIndicate content assignment parameter: when the requested data of user u have stored in sending node m
am,u=1, otherwise, am,u=0.Indicate the relations of distribution between subcarrier and link:Indicate that subcarrier k is distributed to
Sending node m and user u this link, otherwise,em,u=1 expression user u selection sending node m is transmitted,
Otherwise em,u=0.
Three, cluster stability analysis:
The stability of m-th of cluster can be with is defined as:
Wherein, Fm0For the adaptive value of the core customer of m-th of cluster, DmuShow that u-th of the social of user belongs in m-th of cluster
Property value, λ1, λ2For weight coefficient, meet λ1+λ2=1.
The cluster average stability of system can indicate are as follows:
Four, embodiment
In order to assess the D2D multicast cluster-dividing method (referred to as SA-KCA) proposed by the present invention based on user social contact attribute,
By simulation result and cluster algorithm (referred to as DBCA) and tradition K mean value cluster algorithm (referred to as KCA) performance based on distance
It compares.Cluster algorithm (DBCA) basic ideas based on distance are to randomly select user as core customer, then often
Cluster where a user selects nearest core customer with a distance from oneself is added thereto.The base of traditional K mean value cluster algorithm (KCA)
This thinking is identical as this patent, but the update and selection of cluster are carried out according to the Euclidean distance between user.
Fundamental simulation parameter is as shown in table 1:
1. simulation parameter of table
As shown in Figure 1, which depict the relationships between system utility and number of clusters K, it can be seen that SA- proposed by the present invention
KCA can obtain highest system utility.In figure, system utility can achieve 6.7*10 under SA-KCA algorithm9, other three kinds calculations
Under method, system utility is below 6.5*109This is because SA-KCA algorithm not only allows for the Euclidean distance between user, together
When social attribute is taken into account, including the expense of system, the energy residual of user, the factors such as remaining battery, these
It is the important parameter of system utility, therefore, the available guarantee of system utility under SA-KCA algorithm.From this figure it can be seen that
System utility has reduction slightly with the increase of K under SA-KCA, DBCA algorithm, under SA-KCA system utility with K increase
Reduction amplitude very little.This is because increasing with D2D multicast group number, number of users can be reduced in each multicast group, therefore, more
Efficiency reduction is broadcast, consumption number of resources increases, so as to cause the reduction of system utility.And algorithm proposed by the invention, sub-clustering
These consumption can be evaded as far as possible in the process, so that the amplitude of reduction is unobvious.
The curve graph of D2D communication throughput performance and K as shown in Figure 2.It is available to draw a conclusion from figure, firstly,
The system of SA-KCA obtains highest D2D throughput performance, can averagely reach 225Mbps, is always gulped down using the system D2D of DBCA
The amount of spitting performance is taken second place, averagely about 215Mbps.Minimum, the averagely about 125Mbps using the system D2D total throughout of KCA.Its
Secondary, system D2D handling capacity has reduction slightly, D2D handling capacity under SA-KCA with the increase of K under SA-KCA, DBCA algorithm
Amplitude very little is reduced with the increase of K.Finally, under KCA algorithm system D2D handling capacity with the increase of K it is in rising trend, although
Although under KCA system throughput performance will not increasing and reduce with multicast group number K, throughput performance is lower.
Fig. 3 compares the outage probability of user under algorithms of different.As can be seen from the figure: being used under SA-KCA, DBCA system
The outage probability at family has the tendency that increase with K increase.The outage probability of user is minimum under SA-KCA system, DBCA system and
User's outage probability is higher under KCA system.Although this is also demonstrated under KCA system, although system utility and throughput performance will not
Increasing and reduce with multicast group number K, but the performance of user is unable to get preferable guarantee.
Fig. 4 gives the simulation curve of cluster robustness.It can be seen from the figure that the cluster robustness obtained under SA-KCA algorithm
Performance is best, and the robustness of KCA is taken second place, and the cluster robustness obtained under DBCA algorithm is poor.This is because DBCA algorithm is in sub-clustering
In the process there is no factors such as the remaining capacities of mobility and core customer for considering user, cause cluster stability poor.
Can be seen that algorithm proposed by the present invention from the above simulation result can not only improve with system utility and handling capacity
Performance additionally provides higher cluster robustness.
Claims (7)
1. the D2D multicast cluster-dividing method based on user social contact attribute, which comprises the following steps:
S101, K D2D user { u is randomly selected1,…,un,…,uKAssign to { C in K cluster1,…,Ck,…CK}；un(n=
1 ..., K) indicate that n-th of D2D communication user, K are the total number of clusters of D2D multi-casting communication, Ck(k=1 ..., K) indicate k-th of D2D
Multi-casting communication cluster；
S102, the mean vector for calculating each cluster:
Wherein, mkIndicate the mean vector of k-th of cluster, xnIndicate the coordinate of n-th of D2D communication user, ckFor k-th of cluster center
The coordinate of user, | | xn-ck||2Indicate n-th of D2D communication user to k-th of cluster central point Euclidean distance,It is n-th
For a user to the social attribute factor of k-th of cluster central user, ζ and η are proportionality coefficient, meet+η=1 ζ, | Ck| it indicates k-th
D2D number of users in cluster；
S103, to nth user, calculate separately it arrive each cluster sub-clustering Factor distance:
dk,nIndicate that n-th of D2D communication user to the sub-clustering Factor distance of k-th of cluster central user, chooses sub-clustering Factor distance most
Cluster of the close cluster as nth user's final choice, i.e.,
Wherein, C* nFor the cluster number of nth user's selection；
S104, user n is divided into cluster C* nIn；
S105, to k-th of cluster (k=1 ..., K), calculate new mean vector mk′；
S106 judges the new mean vector m of k-th of cluster (k=1 ..., K)k' mean vector the m with current clusterkIt is whether equal；Such as
The unequal m of fruitk≠mk', the mean vector newly calculated is replaced former mean vector by (k=1 ..., K)If equal mk=mk', (k=1 ..., K), the value of former mean vector is without modification；
S107 is returned and is executed S103-S106, until the mean vector phase that the mean vector and previous round of each cluster calculate
Deng algorithmic statement at this time；
S108, it is when by algorithmic statement as a result, as final sub-clustering result.
2. the D2D multicast cluster-dividing method based on user social contact attribute as described in claim 1, which is characterized in that obtain final
After sub-clustering result, in k-th of cluster (k=1 ..., K), the selection of core customer is carried out, method particularly includes: n-th is used
Core customer's fitness F at familynAs user is measured if appropriate for the parameter as core customer, indicate are as follows:
Wherein, α, β,τ is weight parameter, is metEnFor the available battery capacity of nth user, DnFor
The social factor of nth user, PnFor the resident probability of nth user, SnFor the free memory of nth user；
According to the fitness of potential core customer height, core of the highest user of fitness as k-th of cluster in k-th of cluster is selected
Heart node, transmission node of the core node as D2D multi-casting communication；Wherein, nk *For in k-th of cluster
Core customer；FnFor core customer's fitness of user n, ΩkIndicate the set of all D2D users in k-th of cluster.
3. the D2D multicast cluster-dividing method based on user social contact attribute as claimed in claim 2, which is characterized in that described n-th
The social factor D of usernIt is calculate by the following formula:
Wherein,For nth user to the social attribute factor between m-th of user, m ∈ Ωk, m ≠ n m-th of user of expression
It is the user in k-th of cluster, meanwhile, m-th of user and nth user's difference.
4. the D2D multicast cluster-dividing method based on user social contact attribute as claimed in claim 2, which is characterized in that n-th of use
The resident probability P at familyn=Tc,n/Ts,n,
Wherein, Ts,nIndicate total historical time that nth user is resident in all clusters, Tc,nIndicate nth user in current cluster
The interior resident time.
5. the D2D multicast cluster-dividing method based on user social contact attribute as claimed in claim 3, which is characterized in that nth user
It may be expressed as: to the social attribute factor between m-th of user
Wherein, ρ is weight factor,The normalization expression of duration is averagely associated between nth user and m-th of user,The ratio of the total transmission income of the two is accounted for for the D2D communications income of nth user and m-th of user.
6. the D2D multicast cluster-dividing method based on user social contact attribute as claimed in claim 5, which is characterized in that nth user
The normalization that duration is averagely associated between m-th of user indicates:WhereinIt indicates in the Δ t time
Interior, the average duration of content share, specific formula for calculation are as follows between nth user and m-th of user:
Wherein, zn,m(t) the content share state between t moment nth user and m-th of user, z are indicatedn,m(t)=1 show t
Content share state is between moment nth user and m-th of user；Otherwise zn,m(t)=0；δn,m(t) t moment n-th is indicated
Content share value between a user and m-th of user.
7. the D2D multicast cluster-dividing method based on user social contact attribute as claimed in claim 6, which is characterized in that in the Δ t time
Interior, nth user accounts for the ratio of transmission income total therebetween to the D2D communications income of m-th of userIt can be with
It is calculate by the following formula:
Wherein, Cotn,m(t) transmission income of the t moment nth user to m-th of user, Cot are indicatedm,n(t) t moment m is indicated
Transmission income of a user to nth user.
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