CN104486767A - Cluster-based dynamic ABS interference rejection method in heterogeneous cellular network - Google Patents

Abstract

The invention relates to a cluster-based dynamic ABS interference rejection method in a heterogeneous cellular network, and belongs to the technical field of wireless communication. The method comprises the following steps: firstly, calculating interference weights among dense honeycombs and determining an interference relationship among small cellular networks, namely determining whether interference sides exist among the small cellular networks or not so as to establish an interference map of the interference relationship among small cellular networks; then, distributing the small honeycombs to K clusters based on the established small cellular interference map and ensuring that the interference weights among the clusters are the maximum during the distribution; finally, distributing different frequency band resources for different clusters. In order to further reduce intra-cluster interference, a cross type dynamic ABS distributing method is adopted for the intra-cluster small honeycombs, and the intra-cluster small honeycombs are divided into two groups according to loads. According to the loads of the two groups and the average transmission rate information of the user, the ABS proportion among the groups is dynamically adjusted so as to further reduce interference of the intra-cluster small honeycombs and increase the utilization ratio of resources, so that the throughput of the system is improved.

Description

Based on the dynamic ABS disturbance restraining method of sub-clustering in isomery cellular network

Technical field

The invention belongs to wireless communication technology field, relate to the dynamic ABS disturbance restraining method based on sub-clustering in a kind of isomery cellular network.

Background technology

Along with the development of cordless communication network, following wireless network is just towards the direction evolution of intelligent network, broadband, diversification, synthesization.Along with a large amount of of intelligent terminal popularize, will there is explosive growth in data service.In the 5G network in future, data service will mainly be distributed in indoor and hot zones, and this makes super-intensive network become and realizes one of Main Means of 1000 times of traffic demands of following 5G.Super-intensive network can improve the network coverage, significantly capacity, and shunts business, has network design and more efficient channeling more flexibly.In future, towards high band large bandwidth, will adopt the network plan of more crypto set, disposing small-cell/sector will up to more than 100.

Meanwhile, more intensive network design also makes network topology more complicated, and presence of intercell interference has become the principal element that system for restricting capacity increases, and significantly reduces network energy efficiency.

Interference management method in conventional heterogeneous network, mainly go from two angles the problem solving interference: first is the angle from frequency domain, by for different users distributes different resource block and avoid the interference of same frequency range in synchronization, or some derivative methods, such as reduce the power of same some network node of frequency range, thus the interference reduced between same frequency, these class methods consider the division of frequency domain resource, algorithm complex is general higher, and list goes Resources allocation from the angle of frequency domain, a planning on the whole cannot be done to system resource from the time, then cause the unreasonable distribution of resource the long period, another kind of then be the angle from time domain, as eICIC technology, this method is mostly applied to and reduces macrocellular in the interference case of cellulor, range expansion technique is adopted to cellulor, by reducing the power in the subframe of some time slot of macro base station, thus the interference reduced in corresponding cellulor subframe, thus improve the communication quality of user in cellulor to a certain extent, expand the capacity of cellulor, but this kind of method, when cellulor comparatively dense, cannot coordinate the interference between cellulor preferably.

Therefore, in the highly dense cellular network of following 5G, need a kind of effective interference management scheme, can interference in reduction dense cellular network effectively, suitable equilibrium is carried out to the load in dense cellular network simultaneously, thus improve the capacity of 5G system greatly, obtain the availability of frequency spectrum better, improve the service quality of user further.

Summary of the invention

In view of this, the object of the present invention is to provide the dynamic ABS disturbance restraining method based on sub-clustering in a kind of isomery cellular network, the method can be good at the problem of the serious interference improved in dense cellular network.

For achieving the above object, the invention provides following technical scheme:

Based on a dynamic ABS disturbance restraining method for sub-clustering in isomery cellular network, comprise the following steps:

Step one: the interference weight between computation-intensive cellulor, establishes the interference relationships between small cell network, namely whether there is interference edge between cellulor, thus sets up the interference figure about cellulor interference relationships;

Step 2: based on the cellulor interference figure set up, is assigned in K bunch by cellulor, the interference weight between ensureing in the assignment procedure bunch is maximum, be finally the different bunch band resources that distribution is different;

Step 3: the cellulor in bunch, based on load capacity, is divided into two groups, main group of G ₁with from group G ₂, wherein main group of G ₁the load capacity of each cellulor be all higher than from group G ₂each cellulor load capacity, according to principal and subordinate group load be main group of G ₁distributing ABS ratio is θ, from group G ₂distributing ABS ratio is 1-θ.

Further, disturb in step one calculating of weight and interference figure set up criterion and method specifically comprises:

1) number of users added up in cellulor center range is N, and edge customer number is M, and M+N ≠ 0;

2) then served by cell i, be subject to the cellulor regional channel average quality RASQ that community j disturbs _ijbe expressed as:

${\mathrm{RASQ}}_{\mathrm{ij}}=\frac{M*\underset{k=1}{\overset{M}{\mathrm{\Σ}}}{\mathrm{SINR}}_k^O+N*\underset{k=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{SINR}}_k^I}{{(M+N)}^2}---\left(1\right)$

Wherein represent the SINR of cell edge K user, represent the SINR of center of housing estate K user;

3) wherein weight is disturbed if W _ij>=W _th, then namely there is interference edge in two cellulors, wherein W _thfor preset value, MAX (RASQ _ij, RASQ _ji) represent RASQ _ijwith RASQ _jiin maximum value;

4) the interference weight calculated successively between every two cellulors can determine interference figure G (V, E), and wherein V represents cellulor set, and E is interference weights W _ijset;

5) interference figure is periodically updated.

Further, by the interference weights W between two cellulors every in step one known interference figure G (V, E) _ij, system spectral resources is divided into K subsegment, R={R ₁, R ₂..., R _k, be assigned to by point set V in K bunch, between making bunch, weight is maximum, is:

$\max \underset{i=1}{\overset{K-1}{\mathrm{\Σ}}}\underset{j=i+1}{\overset{K}{\mathrm{\Σ}}}\underset{v_1\∈R_i,v_2\∈R_j}{\mathrm{\Σ}}w(v_1,v_2)--\left(2\right)$

Step 2 specifically comprises:

1) initialization: Ω _krepresent the degree of node k, W _ifor a bunch R _iweight, for a bunch R _ihave when algorithm starts there is W _i=0, V' represents remaining cellulor, according to the degree descending sort of cellulor point set, according to Ω _korder is from big to small chosen first to K node from the set of V' and is assigned to K bunch successively;

2) continue according to node Ω _korder-assigned node from big to small in K bunch, if node m is assigned to a bunch R _iin, interference weight in compute cluster $\mathrm{\Δ}{W}_i={\mathrm{\Σ}}_{u\∈R_i}{w}_{m,u};$

3) select bunch in disturb weight minimum bunch R _j, node m is distributed to a bunch R _j, now V'=V'-m;

4) repeat above-mentioned steps 2), 3) until residue cellulor set V' be sky;

5) sub-clustering again during interference figure change.

Further, in step 3 bunch in cellulor specifically comprise based on the dynamic ABS sub-frame configuration scheme of load and user's average service rate:

1) whole system adoption rate fair scheduling algorithm, in bunch, cellulor is divided into two groups of G ₁and G ₂, for G ₁in group, arbitrarily small honeycomb load is greater than G ₂arbitrarily small honeycomb load in group;

2) two groups use intersection subframe to be equipped with scheme, as main group of G ₁when using normal subframe, from group G ₂then use corresponding ABS subframe, until the ABS number of subframes meeting two groups all meets its preset value;

3) suppose for cellulor C _ithe long-time average service rate of middle user j is R _ij, G ₁it is θ, then G that middle ABS subframe is equipped with ratio ₂it is 1-θ that middle ABS subframe is equipped with ratio, 0.4≤θ≤0.6;

4) for full business model θ computational methods:

G ₁group internal burden G ₁number of users Num in group _g1determine, G ₂group internal burden G ₂number of users Num in group _g2determine;

Maximum throughput module is:

$C_{\mathrm{\Σ}}=(1-\mathrm{\θ})*\underset{i\∈G_1}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}{R}_{\mathrm{ij}}+\mathrm{\θ}*\underset{i\∈G_2}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}{R}_{\mathrm{ij}}---\left(3\right)$

Optimization ABS sub-frame allocation it can thus be appreciated that:

${\mathrm{\θ}}_{\mathrm{\Σ}}^{\mathrm{opt}}=\left\{\begin{array}{c}{\mathrm{\θ}}_{\max },\underset{i\∈G_2}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}{R}_{\mathrm{ij}}>\underset{i\∈G_1}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}{R}_{\mathrm{ij}}\\ {\mathrm{\θ}}_{\min },\mathrm{otherwise}\end{array}\right.---\left(4\right)$

5) for non-full business model θ computational methods:

Non-full business load is determined by base station data total amount waiting for transmission;

Suppose base station C _ithe data volume being prepared as user j transmission is B _ij, then main group of G ₁the time that these data have transmitted by middle user j from group G ₂middle user j transmits the time that data need

Total transmission time is T _Σ:

$T_{\mathrm{\Σ}}=\underset{i\∈G_1}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\θ})R_{\mathrm{ij}}}+\underset{i\∈G_2}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}\frac{B_{\mathrm{ij}}}{\mathrm{\θ}*R_{\mathrm{ij}}}---\left(5\right)$

Order $T_{G_1}=\underset{i\∈G_1}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\θ})R_{\mathrm{ij}}},T_{G_2}=\underset{i\∈G_2}{\mathrm{\Σ}}\underset{j\∈C_i}{\mathrm{\Σ}}\frac{B_{\mathrm{ij}}}{\mathrm{\θ}*R_{\mathrm{ij}}},$ Make $\frac{\∂T_{\mathrm{\Σ}}}{\∂\mathrm{\θ}}{|}_{\mathrm{\θ}={\mathrm{\θ}}^{\mathrm{opt}}}=0,$ Have $\frac{{\∂}^2{T}_{\mathrm{\Σ}}}{\∂{\mathrm{\θ}}^2}{|}_{\mathrm{\θ}={\mathrm{\θ}}^{\mathrm{opt}}}>0,$ Therefore T _Σthere is minimum value, θ ^optbest proportion is:

${\mathrm{\θ}}^{\mathrm{opt}}=\frac{\sqrt{T_{G_2}}}{\sqrt{T_{G_2}}+\sqrt{T_{G_1}}}---\left(6\right)$

6) ABS ratio θ is periodically updated.

Beneficial effect of the present invention is: method provided by the invention is set up by cellulor interference figure and the scheme of sub-clustering, reasonably distributes frequency spectrum, the interference of cellulor between inhibit bunch; By the dynamic ABS distribution method based on load, the interference of cellulor in suppressing bunch, and the load in balance bunch, improve the utilance of frequency spectrum.

Accompanying drawing explanation

In order to make object of the present invention, technical scheme and beneficial effect clearly, the invention provides following accompanying drawing and being described:

Fig. 1 is the system architecture schematic diagram that control channel data channel is separated;

Fig. 2 is the overall flow schematic diagram of the method for the invention;

Fig. 3 is that interference figure sets up schematic diagram;

Fig. 4 is sub-clustering schematic flow sheet;

Fig. 5 is spectrum allocation may schematic diagram after sub-clustering;

Fig. 6 is dynamic ABS allocation flow schematic diagram;

Fig. 7 be bunch in ABS configuration schematic diagram.

Embodiment

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.

The present invention is applicable to intensive small cell network, in dense cellular network, not only there is interference between cellulor, and macro base station also also exists stronger interference to cellulor.

As shown in Figure 1, in order to avoid the interference of macro base station, adopt as Fig. 1 system architecture, in this framework, the broadcast of control information is carried out in the special load of macrocellular, and cellulor is then responsible for the transmission carrying out data message specially.First, UE accesses a macrocellular, and then under the assistance of macrocellular, UE is linked into the highest cellulor of average received merit.

Fig. 2 is the overview flow chart of this method, and as can be seen from the figure this method is mainly divided into three main parts:

Step 1: calculate the average area channel quality RASQ between cellulor _ij, obtain the interference weights W between cellulor _ij, establish interference figure G (V, E);

Step 2: sub-clustering is carried out to cellulor according to interference figure G (V, E), and different frequency spectrum resources is used to the cellulor in different bunches;

Step 3: based on load, user's average transmission rate information to bunch in cellulor carry out dynamic ABS pro rate.

Consider user mobility, and the change of load, need repeat above-mentioned steps to periodicity T, in the short period of time, user can think static and user load change can be similar to and thinks constant, therefore T is set to the short period, and the time of considering shortlyer can increase overhead, calculate too frequent, T is set to 1s, specifically can regulate according to actual conditions, as user moving speed etc.

Step 1 is described in detail as follows:

Step 1.1: the number of users first added up in cellulor statistics cellulor center range is N, and edge customer number is M: user measures RSRP > RSRP _ththen user centered by statistics; User measures RSRP < RSRP _ththen statistics is edge customer; RSRP _thfor preset value.

Step 1.2: the zone leveling channel quality RASQ being subject to the cellulor i that cellulor j disturbs _ijbe expressed as:

${\mathrm{RASQ}}_{\mathrm{ij}}=\frac{M*\underset{k=1}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{SINR}}_k^O+N*\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{SINR}}_k^I}{{(M+N)}^2}---\left(1\right)$

Wherein represent the SINR of edge customer, represent Cell Center User SINR;

Step 1.3: calculate the interference weight between two adjacent cellulors:

$W_{\mathrm{ij}}=\frac{1}{\mathrm{MAX}({\mathrm{RASQ}}_{\mathrm{ij}},{\mathrm{RASQ}}_{\mathrm{ji}})}---\left(2\right)$

Step 1.4: if W _ij>=W _ththen interference edge exists, and setting up with cellulor is summit V, interference weights W _ijfor the interference figure G (V, E) on limit, and upgrade interference figure with cycle T.

Fig. 3 is that interference figure sets up schematic diagram

As shown in Figure 3, M _irepresent the number of users at cellulor i edge, N _irepresent the number of users at cellulor i center, suppose that the distance between No. 1,2,3, cellulor is equidistant, i.e. d ₁₂=d ₁₃=d ₂₃, and the transmitting power P of three cellulors ₁=P ₂=P ₃;

The relation of three cellulor sums has: M ₁+ N ₁=M ₂+ N ₂=M ₃+ N ₃;

Edge customer quantitative relation has: M ₁< M ₂< M ₃;

The interference be subject to due to edge customer in cellulor is comparatively large, and when cellulor amount of edge is more, the disturbed user of cellulor concentrates on fringe region; Suppose that all SINR of edge customer that first goes are identical, the SINR of central user is identical, has according to the release of being not difficult of above-mentioned flow process:

1, No. 2 cellulor is had: RASQ ₁₂> RASQ ₂₁, then

2, No. 3 cellulors are had: RASQ ₂₃> RASQ ₃₂, then

1, No. 3 cellulor is had: RASQ ₁₂> RASQ ₃₁, then

And W ₁₃=W ₂₃> W ₁₂;

It can thus be appreciated that, when user is more be distributed in cell edge time, we will choose the more community of Cell Edge User as the important evidence passing judgment on interference weight, be subject to stronger interference when just can Cell Edge User be avoided preferably more in the process of sub-clustering like this, cause marginal user performance poor.

Step 2 is described in detail as follows:

Fig. 4 is the detailed process of interference figure sub-clustering, as shown in the figure:

Step 2.1: initialization: Ω _krepresent the degree of node k, W _ifor a bunch R _iweight, for a bunch R _ihave when algorithm starts there is W _i=0;

Step 2.2:V' represents remaining cellulor, according to the degree descending sort of cellulor point set, and another i=0;

Step 2.3: choose Ω in set V' _kmaximum element m distributes;

Step 2.4: judge that whether K bunch exist empty set, is continue step 2.5, otherwise jumps to step 2.6;

Step 2.5: element m is assigned to R _iin, i=i+1, returns step 2.4;

Step 2.6: the interference weight calculating element m and each bunch choose interference weight minimum bunch, element m is distributed to this bunch;

Step 2.7: repeat above-mentioned steps 2.3 to step 2.5 until residue cellulor set V' is empty.

Fig. 5 is spectrum allocation may schematic diagram after sub-clustering:

After sub-clustering terminates, the interference between bunch is maximum, and the interference weight in bunch is less, and therefore we are to different bunch R ₁, R ₂, R ₃, R ₄use different frequency ranges 1,2,3,4; Interference larger between just can eliminating bunch, still also exists interference now bunch, we will in subsequent step to bunch in disturb and suppress.

Step 3 is described in detail as follows:

With reference to Fig. 6, dynamic ABS distribution method in bunch:

Step 3.1: statistics bunch in all users measure SINR, it is P that user accepts power, and interference gross power is I, and noise power is N ₀, then SINR can be expressed as:

$\mathrm{SINR}=\frac{P}{I+N_0}---\left(3\right)$

Step 3.2: consider that ABS subframe θ regulating cycle is shorter, its instantaneous service speed is approximately equal to the average service rate in the cycle, and therefore the computational methods of Mean Speed can simplify as follows:

SINR is measured to user and carries out CQI mapping, and obtain its modulation coding mode, according to modulation coding mode, calculate the size of its transmission block, its service speed R can be obtained; Or by aromatic formula simple computation: R=log (1+SINR), can obtain average service rate R;

Step 3.3: the cellulor in bunch, based on load capacity, is divided into two groups, main group of G ₁with from group G ₂, wherein main group of G ₁the load capacity of each cellulor be all higher than from group G ₂each cellulor load capacity;

Bunch internal burden module:

For full business model:

Due to bunch in all users need the traffic carrying capacity transmitted all near infinite be large, therefore, we using number of users as the standard weighing load, G ₁group internal burden G ₁number of users in group determine, G ₂group internal burden G ₂number of users in group determine;

For non-full business model:

Bunch internal burden is determined by the total traffic needing cellulor to transmit, and this load is provided by concrete business model;

Step 3.4: the load according to principal and subordinate's group is main group of G ₁distributing ABS subframe ratio is θ, from group G ₂distributing ABS subframe ratio is 1-θ, 0.4≤θ≤0.6;

For full business model θ computational methods:

Under full business model, the total amount of data that user will be transmitted is unlimited, the transmission quantity therefore in the unit interval and throughput C _Σbe the important measure standard of gauging system performance, therefore set up module C _Σas follows, another C _Σget maximum, this thing θ is then optimum ABS subframe ratio;

Maximum throughput module is:

$C_{\mathrm{\&Sigma;}}=(1-\mathrm{\&theta;})*\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}+\mathrm{\&theta;}*\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}---\left(4\right)$

Optimization ABS sub-frame allocation it can thus be appreciated that:

${\mathrm{\&theta;}}_{\mathrm{\&Sigma;}}^{\mathrm{opt}}=\left\{\begin{array}{c}{\mathrm{\&theta;}}_{\max },\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}>\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}\\ {\mathrm{\&theta;}}_{\min },\mathrm{otherwise}\end{array}\right.---\left(5\right)$

For non-full business model θ computational methods:

Non-full business load is determined by base station data total amount waiting for transmission;

Suppose base station C _ithe data volume being prepared as user j transmission is B _ij, then main group of G ₁the time that these data have transmitted by middle user j from group G ₂middle user j transmits the time that data need for non-full business model, total business volume is in dynamic change, and user's average service rate is also in change, but generally speaking total transmission time is the smaller the better, therefore sets up and builds total transmission time function T _Σ, now another T _Σminimum, the θ's obtained is then most ABS subframe ratio;

Total transmission time is T _Σ:

$T_{\mathrm{\&Sigma;}}=\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\&theta;})R_{\mathrm{ij}}}+\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{\mathrm{\&theta;}*R_{\mathrm{ij}}}---\left(6\right)$

Order $T_{G_1}=\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\&theta;})R_{\mathrm{ij}}},T_{G_2}=\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{\mathrm{\&theta;}*R_{\mathrm{ij}}},$ Make $\frac{\&PartialD;T_{\mathrm{\&Sigma;}}}{\&PartialD;\mathrm{\&theta;}}{|}_{\mathrm{\&theta;}={\mathrm{\&theta;}}^{\mathrm{opt}}}=0,$ Have $\frac{{\&PartialD;}^2{T}_{\mathrm{\&Sigma;}}}{\&PartialD;{\mathrm{\&theta;}}^2}{|}_{\mathrm{\&theta;}={\mathrm{\&theta;}}^{\mathrm{opt}}}>0,$ Therefore T _Σthere is minimum value, θ ^optbest proportion is:

${\mathrm{\&theta;}}^{\mathrm{opt}}=\frac{\sqrt{T_{G_2}}}{\sqrt{T_{G_2}}+\sqrt{T_{G_1}}}---\left(7\right)$

Step 3.6: above-mentioned solving is obtained ABS subframe and be configured:

With reference to Fig. 7, ABS sub-frame configuration scheme:

ABS subframe optimal model as from the foregoing, the allocation proportion θ of ABS subframe in obtaining bunch, in order to interference in reducing further bunch, uses two groups and intersects subframes and be equipped with schemes:

In a frame structure, suppose that being used for the subframe of normal data transfer is 8, above-mentionedly solve rear G ₁group θ=0.4, then G ₂group θ=0.6, G ₁in group, ABS number of subframes can be approximately 3, G ₂aBS number of subframes in group can be approximately 4, as can be seen from Fig. 7 we, as main group of G ₁when using normal subframe, from group G ₂then use corresponding ABS subframe, until the ABS number of subframes meeting two groups all meets its preset value.

What finally illustrate is, above preferred embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although by above preferred embodiment to invention has been detailed description, but those skilled in the art are to be understood that, various change can be made to it in the form and details, and not depart from claims of the present invention limited range.

Claims (4)

1. in isomery cellular network based on a dynamic ABS disturbance restraining method for sub-clustering, it is characterized in that: comprise the following steps:

Step one: the interference weight between computation-intensive cellulor, establishes the interference relationships between small cell network, namely whether there is interference edge between cellulor, thus sets up the interference figure about cellulor interference relationships;

Step 2: based on the cellulor interference figure set up, is assigned in K bunch by cellulor, the interference weight between ensureing in the assignment procedure bunch is maximum, be finally the different bunch band resources that distribution is different;

Step 3: the cellulor in bunch, based on load capacity, is divided into two groups, main group of G ₁with from group G ₂, wherein main group of G ₁the load capacity of each cellulor be all higher than from group G ₂each cellulor load capacity, according to principal and subordinate group load be main group of G ₁distributing ABS ratio is θ, from group G ₂distributing ABS ratio is 1-θ.

2. in a kind of isomery cellular network according to claim 1 based on the dynamic ABS disturbance restraining method of sub-clustering, it is characterized in that: disturb in step one calculating of weight and interference figure set up criterion and method specifically comprises:

1) number of users added up in cellulor center range is N, and edge customer number is M, and M+N ≠ 0;

2) then served by cell i, be subject to the cellulor regional channel average quality RASQ that community j disturbs _ijbe expressed as:

${\mathrm{RASQ}}_{\mathrm{ij}}=\frac{M*\underset{k=1}{\overset{M}{\mathrm{\&Sigma;}}}{\mathrm{SINR}}_k^O+N*\underset{k=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{SINR}}_k^I}{{(M+N)}^2}---\left(1\right)$

Wherein represent the SINR of cell edge K user, represent the SINR of center of housing estate K user;

3) wherein weight is disturbed if W _ij>=W _th, then namely there is interference edge in two cellulors, wherein W _thfor preset value, MAX (RASQ _ij, RASQ _ji) represent RASQ _ijwith RASQ _jiin maximum value;

4) the interference weight calculated successively between every two cellulors can determine interference figure G (V, E), and wherein V represents cellulor set, and E is interference weights W _ijset;

5) interference figure is periodically updated.

3. in a kind of isomery cellular network according to claim 2 based on the dynamic ABS disturbance restraining method of sub-clustering, it is characterized in that: by the interference weights W between two cellulors every in step one known interference figure G (V, E) _ij, system spectral resources is divided into K subsegment, R={R ₁, R ₂..., R _k, be assigned to by point set V in K bunch, between making bunch, weight is maximum, is:

$\max \underset{i=1}{\overset{K-1}{\mathrm{\&Sigma;}}}\underset{j=i+1}{\overset{K}{\mathrm{\&Sigma;}}}\underset{v_1\&Element;R_i,v_2\&Element;R_j}{\mathrm{\&Sigma;}}w(v_1,v_2)---\left(2\right)$

Step 2 specifically comprises:

1) initialization: Ω _krepresent the degree of node k, W _ifor a bunch R _iweight, for a bunch R _ihave when algorithm starts there is W _i=0, V ' represents remaining cellulor, according to the degree descending sort of cellulor point set, according to Ω _korder is from big to small chosen first to K node from the set of V ' and is assigned to K bunch successively;

2) continue according to node Ω _korder-assigned node from big to small in K bunch, if node m is assigned to a bunch R _iin, interference weight in compute cluster $\mathrm{\&Delta;}{W}_i={\mathrm{\&Sigma;}}_{u\&Element;R_i}{w}_{m,u};$

3) select bunch in disturb weight minimum bunch R _j, node m is distributed to a bunch R _j, now V '=V '-m;

4) repeat above-mentioned steps 2), 3) until residue cellulor set V ' be sky;

5) sub-clustering again during interference figure change.

4. in a kind of isomery cellular network according to claim 1 based on the dynamic ABS disturbance restraining method of sub-clustering, it is characterized in that: in step 3 bunch in cellulor specifically comprise based on the dynamic ABS sub-frame configuration scheme of load and user's average service rate:

1) whole system adoption rate fair scheduling algorithm, in bunch, cellulor is divided into two groups of G ₁and G ₂, for G ₁in group, arbitrarily small honeycomb load is greater than G ₂arbitrarily small honeycomb load in group;

2) two groups use intersection subframe to be equipped with scheme, as main group of G ₁when using normal subframe, from group G ₂then use corresponding ABS subframe, until the ABS number of subframes meeting two groups all meets its preset value;

3) suppose for cellulor C _ithe long-time average service rate of middle user j is R _ij, G ₁it is θ, then G that middle ABS subframe is equipped with ratio ₂it is 1-θ that middle ABS subframe is equipped with ratio, 0.4≤θ≤0.6;

4) for full business model θ computational methods:

G ₁group internal burden G ₁number of users in group determine, G ₂group internal burden G ₂number of users in group determine;

Maximum throughput module is:

$C_{\mathrm{\&Sigma;}}=(1-\mathrm{\&theta;})*\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}+\mathrm{\&theta;}*\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}---\left(3\right)$

Optimization ABS sub-frame allocation it can thus be appreciated that:

${\mathrm{\&theta;}}_{\mathrm{\&Sigma;}}^{\mathrm{opt}}=\left\{\begin{array}{c}{\mathrm{\&theta;}}_{\max },\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}>\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}{R}_{\mathrm{ij}}\\ {\mathrm{\&theta;}}_{\min },\mathrm{otherwise}\end{array}\right.---\left(4\right)$

5) for non-full business model θ computational methods:

Non-full business load is determined by base station data total amount waiting for transmission;

Suppose base station C _ithe data volume being prepared as user j transmission is B _ij, then main group of G ₁the time that these data have transmitted by middle user j from group G ₂middle user j transmits the time that data need

Total transmission time is T _Σ:

$T_{\mathrm{\&Sigma;}}=\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\&theta;})R_{\mathrm{ij}}}+\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{\mathrm{\&theta;}*R_{\mathrm{ij}}}---\left(5\right)$

Order $T_{G_1}=\underset{i\&Element;G_1}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{(1-\mathrm{\&theta;})R_{\mathrm{ij}}},T_{G_2}=\underset{i\&Element;G_2}{\mathrm{\&Sigma;}}\underset{j\&Element;C_i}{\mathrm{\&Sigma;}}\frac{B_{\mathrm{ij}}}{\mathrm{\&theta;}*R_{\mathrm{ij}}},$ Make $\frac{\&PartialD;T_{\mathrm{\&Sigma;}}}{\&PartialD;\mathrm{\&theta;}}{|}_{\mathrm{\&theta;}={\mathrm{\&theta;}}^{\mathrm{opt}}}=0,$ Have $\frac{{\&PartialD;}^2{T}_{\mathrm{\&Sigma;}}}{\&PartialD;{\mathrm{\&theta;}}^2}{|}_{\mathrm{\&theta;}={\mathrm{\&theta;}}^{\mathrm{opt}}}>0,$ Therefore T _Σthere is minimum value, θ ^optbest proportion is:

${\mathrm{\&theta;}}^{\mathrm{opt}}=\frac{\sqrt{T_{G_2}}}{\sqrt{T_{G_2}}+\sqrt{T_{G_1}}}---\left(6\right)$

6) ABS ratio θ is periodically updated.