CN106231610B - Based on the resource allocation methods of sub-clustering in Femtocell double-layer network - Google Patents
Based on the resource allocation methods of sub-clustering in Femtocell double-layer network Download PDFInfo
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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- H04W72/04—Wireless resource allocation
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Abstract
The present invention relates to the resource allocation methods based on sub-clustering in the Femtocell double-layer network of Femto cell, are that macro user MUEs distributes subchannel including the use of three-wheel subchannel distribution algorithm；According to the object of planning and constraint condition of power distribution, use classical water-filling algorithm for MUEs distribution power；Use improved Global Genetic Simulated Annealing Algorithm GASA for Femto cell sub-clustering；According to the rate requirement of femto user FUEs, heuritic approach is used to distribute subchannel for FUEs；And power distribution is carried out to FUEs using KKT condition.The present invention minimizes the interference between FUEs, improves the availability of frequency spectrum, ensure that the service quality of FUEs and MUEs under the premise of guaranteeing MUEs normal communication.
Description
Technical field
The present invention relates to wireless communication technology fields, in particular to are based in Femto cell Femtocell double-layer network
The resource allocation methods of sub-clustering.
Background technique
With the third generation (the 3rdGeneration, 3G) mobile communication system compares, long term evolution (Long Term
Evolution, LTE) the spectral frequencies utilization rate of system increases, but its path loss is increased compared to 3G system,
Therefore LTE system cannot provide good in-door covering.Research shows that nearly 90% data service and 60% voice service hair
It is raw indoors and hot spot region thus how good in-door covering to be provided and satisfied service quality is provided for user, it is fortune
Seek the current urgent problem to be solved of quotient.
By introducing Femto cell in classical macro-cellular coverage area, Femtocell double-layer network is formed, it has also become
The effective measures of indoor wireless communication technical problem are solved at present.Femto cell is as short distance, the family of low-power, low cost
Front yard cell is deployed through Digital Subscriber Loop (Digital Subscriber Loop, DSL) by user or optical fiber is connected to core
Heart net can not only provide preferably indoor experience, additionally it is possible to unload macrocell network flow, and increase network for user
Coverage area.However the characteristics such as frequency spectrum are shared due to the non-planning of femtocell network, random access and with macrocell, it will
Cause its cross-layer interference problem between macrocell and with use same channel other Femto cells between same layer
Interference problem, therefore how to reduce above two interference, it is to need urgently to study and solve the problems, such as.
Some methods interfered for reducing cross-layer interference with same layer have been proposed in presently relevant document, wherein centralization
Interference management scheme is the effective hand for inhibiting to interfere in bilayer Femtocell network using partial frequency multiplexing and power control
Section.In addition, it is thus proposed that a kind of packet-based interference management scheme, method particularly includes: group technology is divided into orthogonal in group
Orthogonal grouping between grouping and group organizes interior orthogonal group technology and divides the Femto cell of serious interference in identical group, identical group
Femto cell use different subchannels, different groups can be multiplexed identical subchannel；On the contrary, orthogonal group technology between group
It is that will not interfere with or interfere Femto cell of the Femto cell of very little point in identical group, identical group can be multiplexed identical
Subchannel, different component match different subchannels.
Inventors have found that in the prior art, centralized interference management scheme with Femto cell quantity increase,
Computation complexity can also sharply increase, so that this method is difficult to apply in the scene of Femto cell dense deployment；Meanwhile base
Orthogonal group technology is grouped from each Femto cell itself in organizing in the interference management scheme of grouping, it is difficult to
Find it is global be preferably grouped scheme, meanwhile, the Femto cell number in this grouping scheme obtain each group is very uneven
Weighing apparatus, so that a part of femto user (Femtocell User Equipments, FUEs) cannot be assigned to enough son letters
Road, to be difficult to ensure the service quality (Quality of Service, QoS) of FUEs.
Summary of the invention
For the above problem of the prior art, present invention discusses the resource allocation problem in Femtocell double-layer network,
It proposes the resource allocation methods based on sub-clustering in a kind of Femtocell double-layer network, cross-layer interference and same can be effectively inhibited
Layer interference, and it is able to satisfy the QoS demand of macro user (Macrocell User Equipments, MUEs) and FUEs.
A kind of resource allocation methods for Femtocell double-layer network based on sub-clustering of the present invention, comprising the following steps:
Step 101: subchannel distribution being executed to macro user MUEs using three-wheel subchannel distribution algorithm；
Step 102: the object of planning and constraint condition distributed according to macro user power use classical water-filling algorithm for MUEs
Distribution power；
Step 103: using improved Global Genetic Simulated Annealing Algorithm GASA for Femto cell sub-clustering；
Step 104: according to the rate requirement of femto user FUEs, heuritic approach being used to distribute subchannel for FUEs；
Step 105: needing-Ku En-Plutarch (Karush-Kuhn-Tucker, KKT) condition to femto user using Caro
FUEs carries out power distribution.
Preferably, the step 101 executes subchannel distribution packet to macro user MUEs using three-wheel subchannel distribution algorithm
Include: the more new formula that reference shannon formula models macro user m data rate isIts
In, M is macro total number of users, and K is subchannel sum,For Signal to Interference plus Noise Ratio of the macro user m on subchannel k, Δ f is channel strip
It is wide；And then consider the data rate request of macro user, it is that macro user distributes son letter under the premise of meeting macro user rate section
Road.
Preferably, described is that macro user distributes subchannel under the premise of meeting macro user rate section, comprising:
Step 101A: traversing all subchannels, finds out the subchannel k for enabling macro user m to obtain maximum channel gain, and will
Subchannel k distributes to macro user m, if the rate of obtained macro user m meets its minimum speed limit demand, macro user m no longer joins
Add channel distribution, and then if all macro user data rates being calculated are all satisfied minimum speed limit demand, exits circulation；
Step 101B: if subchannel has residue, repeat step 101A, macro user rate Rule of judgment becomes sentencing
Whether the rate of the disconnected corresponding macro user being calculated meets its flank speed demand；
Step 101C: if subchannel still has residue, repeating step 101A, no longer carries out macro user data rate and sentences
It is disconnected.
Preferably, the object of planning and constraint condition that the step 102 is distributed according to macro user power are filled the water using classics
Algorithm is that MUEs distribution power includes:
To maximize power system capacity as optimization aim, maximum general power is constraint condition, constructs the power distribution mesh of MUEs
Scalar functions:And meet constraint condition:
It uses water-filling algorithm for macro user's distribution power, obtains
Wherein,η=Δ f/ ζ ln2 is water line；
It is the gain interference ratio on subchannel k,Indicate channel gain of the macro base station to macro user m on subchannel k,
Indicate femto base station FBSjTo channel gain of the macro user m on subchannel k；Indicate femto base station FBSjIn son
Transmission power on channel k；σ2For noise power；ζ is Lagrange multiplier, is constant；It is macro base station on subchannel k
Transmission power,For total transmission power, Δ f is channel width, and M is macro total number of users, and K is subchannel sum,.
Preferably, the step 103 uses the improved Global Genetic Simulated Annealing Algorithm GASA to include: for Femto cell sub-clustering
Using the interference summation minimum between the femto base station FBSs in same cluster as objective function, modeling optimization equation:And meet constraint condition:Cg∩Cn=Φ (g, n ∈ χ, g ≠ n)
And xin∈{0,1}；Wherein, χ={ 1 ..., NAIndicate the set of cluster, F and NARespectively indicate the quantity of femto base station FBSs
With the quantity of cluster；wijIt is FBSiAnd FBSjBetween interference weight；xinIt is the sub-clustering oriental matrix of FBSs, works as xinWhen=1, indicating will
FBSiN-th of cluster is assigned to, x is worked asinWhen=0, i.e. expression FBSiN-th of cluster is not assigned to；CnIndicate the set of FBSs in n-th of cluster, Cg
Indicate the set of FBSs in g-th of cluster,For the set of FBSs total in system,And then it uses
Global Genetic Simulated Annealing Algorithm solves the problems, such as this sub-clustering.
Preferably, the step 104 uses heuritic approach for FUEs points according to the rate requirement of femto user FUEs
Sub-channel includes: to maximize the data rate of FUEs as optimization aim:
And meet constraint condition Wherein,
DjFor FBSjThe FUEs of service gathers；Δ f is channel width；For FBSjThe femto user u of service is on subchannel k
Signal to Interference plus Noise Ratio；For FBSjThe rate requirement of the femto user u of service；If ak,n=1, indicate that subchannel k distributes to cluster Cn,
Otherwise, ak,n=0；K is subchannel sum.
Preferably, the step 105 needs-Ku En-Plutarch (Karush-Kuhn-Tucker, KKT) condition pair using Caro
It includes: to establish function model by optimization aim of maximize handling capacity that femto user FUEs, which carries out power distribution:
And
Wherein,Indicate FBSjTo the transmission power of user u on subchannel k,It indicates
The set of MUEs,It is the gain on subchannel k
Interfere ratio, when femto user u is in subchannel distribution it has been determined thatFor FBSjChannel gain on subchannel k,WithRespectively FBSiWith macro base station to the channel gain of femto user u,WithRespectively FBSiWith macro base
The transmission power stood on channel k, σ2For noise power.
In constraint condition C1,For FBSjTotal transmission power, then C1 indicates FBSjTransmitting function in all subchannels
The sum of rate is not more than FBSjTotal transmission power；In constraint condition C2, RjFor FBSjMinimum-rate demand, then C2 indicate FBSj
In all subchannels transmission rate and be not less than its minimum-rate demand；In constraint condition C3, ξk,eIndicate femto in cluster
User u by other femtos user interference threshold,Respectively indicate femto base station FBSjTo macro user e in subchannel k
On channel gain, then C3 indicate FBSjThe femto user u serviced is by FBSjThe interference summation of the other users serviced
No more than the interference threshold of femto user u；In constraint condition C4, ξk,mIt is femto user u by the interference door of macro user
Limit,Respectively indicate femto base station FBSjTo channel gain of the macro user m on subchannel k, then C4 indicates FBSjInstitute
The interference threshold that the femto user u of service is interfered summation to be not more than femto user u by macro user.
Preferably, described to further comprise to FUEs progress power distribution using KKT condition: according to FUEs power distribution
Optimization object function and constraint condition, reference KKT condition obtain:
Wherein, wherein α, β, θ and ε are Lagrange multipliers, are constant； For water line.
The beneficial effects of the present invention are: the present invention is being protected for the resource allocation problem in Femtocell double-layer network
Under the premise of demonstrate,proving macro user QoS, using the subchannel and power for completing MUEs and FUEs based on the resource allocation algorithm of sub-clustering
Distribution, and restrained effectively cross-layer interference and same layer interference, the availability of frequency spectrum can not only be improved, more can guarantee FUEs and
The QoS demand of MUEs.
Detailed description of the invention
Fig. 1 is the resource allocation preferred embodiment flow diagram based on sub-clustering in Femtocell double-layer network of the present invention；
Fig. 2 is MUEs resource allocation algorithm module exemplary diagram in Femtocell double-layer network of the present invention；
Fig. 3 is the implementation of FBSs clustering process in the resource allocation based on sub-clustering in Femtocell double-layer network of the present invention
Example flow chart；
Fig. 4 is present invention figure compared with the emulation of prior art MUEs outage probability；
Fig. 5 is present invention figure compared with prior art MUEs average throughput；
Fig. 6 is present invention figure compared with the emulation of prior art Femtocell spectrum efficiency；
Fig. 7 is present invention figure compared with the emulation of prior art FUEs fairness；
Fig. 8 is present invention figure compared with the emulation of prior art FUEs satisfaction.
Specific embodiment
To make the object, technical solutions and advantages of the present invention express to be more clearly understood, with reference to the accompanying drawing and specifically
Case study on implementation is described in further details the present invention.
Resource allocation methods preferred embodiment flow chart in Fig. 1 Femtocell double-layer network of the present invention based on sub-clustering, should
Method the following steps are included:
Step 101: subchannel distribution being executed to macro user MUEs using three-wheel subchannel distribution algorithm；
Step 102: the object of planning and constraint condition distributed according to macro user power use classical water-filling algorithm for MUEs
Distribution power；
Step 103: sub-clustering is executed to femto base station FBSs using improved Global Genetic Simulated Annealing Algorithm GASA；
Step 104: according to the rate requirement of femto user FUEs, subchannel point being executed to FUEs using heuritic approach
Match；
Step 105: power distribution being carried out to femto user FUEs using KKT condition.
Fig. 2 is MUEs resource allocation function module in Femtocell double-layer network of the present invention, comprising:
201, subchannel distribution is executed to MUEs, concrete function is realized are as follows:
It is assumed that carrying out subchannel distribution to MUEs under the premise of power averaging distribution, then macro user m is on subchannel k
Signal to Interference plus Noise Ratio are as follows:
Wherein,WithRespectively macro base station and femto base station j (FBSj) transmission power on subchannel k；WithRespectively indicate the channel gain of macro base station and FBSj to macro user m on subchannel k；Indicate the set of FBSs,Indicate the set of subchannel,Indicate MUEs set,σ2For noise power.
For the data rate request for meeting macro user m, a kind of subchannel distribution calculation for meeting macro user rate section is provided
Method quotes the more new formula that shannon formula models macro user m data rate according to formula (1) are as follows:
WhereinFor Signal to Interference plus Noise Ratio of the macro user m on subchannel k, Δ f is channel width.
The above-mentioned MUEs subchannel distribution problem provided can be solved by three-wheel subchannel distribution algorithm, specific implementation
Step includes:
201A: traversing all subchannels, gives channel gain maximum macro user each subchannel distribution, if some macro use
Family meets the request of its minimum data rate, i.e. Rm′≥Rm,min, then the macro user exits subchannel distribution, wherein Rm′Indicate macro
The current instant data rate of user m, Rm,minIndicate the minimum data rate request of macro user m；
201B: if subchannel has residue, continuing to distribute, if some macro user meets peak data rate request, i.e.,
Rm′≥Rm,max, then the macro user exits subchannel distribution, wherein Rm,maxIndicate the peak data rate request of macro user m；
201C: if subchannel still has residue, continuing subchannel distribution, no longer progress user data rate judgement.
202, MUEs power distribution, concrete function are realized are as follows:
After obtaining MUEs sub-channel assignment result, to further increase system performance, using classical water-filling algorithm to initial
The power of mean allocation re-starts adjustment, and to maximize power system capacity as optimization aim, maximum general power is constraint condition, structure
Build the power distribution objective function of MUEs are as follows:
Wherein,It is the gain interference ratio on subchannel k,For femto
The set of base station,Indicate channel gain of the macro base station to macro user m on subchannel k,Indicate femto base station
FBSjTo channel gain of the macro user m on subchannel k；Indicate femto base station FBSjTransmitting function on subchannel k
Rate；For transmission power of the macro base station on subchannel k, constraint condition (4) is transmitting function of the macro base station in all subchannels
Rate summation is not more than total transmission power, i.e.,WhereinFor the total transmission power of macro base station.
Above-mentioned MUEs power distribution problems will be implemented using classical water-filling algorithm are as follows: according to MUEs power distribution
Optimization object function (3) and constraint condition (4), and Lagrange's equation is constructed using lagrange's method of multipliers are as follows:
Wherein, ζ is Lagrange multiplier, is constant, by above-mentioned Lagrange's equation (5) to transmission powerIt solves inclined
It leads, i.e.,K equation then can be obtained, and it is converted, then obtain following relational expression:
Wherein,η is water line, and value is η=Δ f/ ζ ln2；It therefore can
Quickly find out the transimission power on every sub-channels.,For the set of femto base station FBSs total in system,
FUEs resource allocation algorithm in Femtocell double-layer network of the present invention, comprising:
301, sub-clustering is carried out to FBSs, concrete function is realized are as follows:
It defining non-directed graph G=[V, E, W], V is vertex, F FBSs is represented,E is to link each vertex
Side, W={ wijRepresentative edge weight, wherein i, j ∈ { 1 ..., F }, weight show more greatly the interference between corresponding femto base station
It is bigger, and have wij=ρjipi/ρjjpj+ρijpj/ρiipi.The target of sub-clustering optimization is according to wijSize will interfere each other it is small
Femto base station FBS point in same cluster, interfere big femto base station FBSs points to carry out establishment in different clusters each other.Benefit
F FBSs is assigned into N with graph coloring principleAA cluster, χ={ 1 ..., NA, and it is minimum with the interference summation between the FBSs in same cluster
As objective function, indicate are as follows:
Wherein, F and NARespectively indicate the quantity of femto base station FBSs and the quantity of cluster；wijIt is FBSiAnd FBSjBetween it is dry
Disturb weight；xinIt is the sub-clustering oriental matrix of FBSs, works as xinWhen=1, FBS is indicatediN-th of cluster is assigned to, conversely, xin=0, i.e. table
Show FBSiN-th of cluster is not assigned to；In constraint condition C1, CnIndicate the set of FBSs in n-th of cluster,It is total in system
The set of FBSs,Then constraint condition C1 indicates distribution to NAThe total quantity of FBSs is F in a cluster；About
Beam condition C 2 indicates that all FBSs in Femtocell double-layer network are assigned in different clusters, that is, requires in each cluster
There cannot be the FBSs of overlapping；Constraint condition C3 indicates xinFor binary number, value is 0 or 1.
Based on the above-mentioned object of planning (7) and constraint condition (8), proceeded from the situation as a whole dynamically using Global Genetic Simulated Annealing Algorithm
FBSs is grouped, is preferably grouped scheme until finding one, specific implementation flow is as shown in Figure 3, comprising:
103A: initialization: population at individual size sizepop, maximum evolution number MAXGEN, crossover probability Pc, variation is generally
Rate Pm, anneal initial temperature T0, temperature cooling ratio k, final temperature Tend；
103B: it is random to generate initial population Chrom, calculate fitness value f individual in populationi, wherein setting fiFor planning
Target；
103C: the genetic manipulations such as selection, intersection and variation are implemented to population Chrom, that is, generate new FBS sub-clustering result；
Its fitness value f is calculated to the new individual of generationi', if fi′<fi, then old individual is replaced with new individual, otherwise, with probability exp
((fi-fi')/T) receive new individual；
103D: if evolution number gen < MAXGEN, gen=gen+1, go to step 103C, otherwise, step is gone to
103E；
103E: if Ti<Tend, then algorithm terminates, and returns to globally optimal solution, otherwise, executes cooling operation Ti+1=kTi, go to
Step 103B.
302, subchannel distribution is executed to FUEs, concrete function is realized are as follows:
In order to effectively avoid FBSs multipling channel in different clusters that serious same layer is brought to interfere, the present invention is that different clusters distribute just
The subchannel of friendship can be multiplexed identical subchannel with FBSs in cluster, plan as follows:
Wherein, NAThe quantity of cluster and the sum of subchannel, C are respectively indicated with KnIndicate the set of FBSs in n-th of cluster, Dj
Indicate FBSjThe FUEs of service gathers,Indicate FBSjLetter of the femto user FUE u of service on subchannel k is dry to make an uproar
Than；ak,nIndicate whether subchannel k distributes to cluster Cn, work as ak,n=1, subchannel k distributes to cluster Cn；Otherwise, ak,n=0, that is, it indicates
Subchannel k is not assigned to cluster Cn, then constraint condition (10) shows that all subchannels must and be only capable of one group of selection and be allocated；For FBSjThe rate requirement of the femto user FUE u of service, i.e. constraint condition (11) show to distribute to femto user
The data rate of FUE u meets its own rate requirement.
According to the object of planning (9) and constraint condition (10) (11), above-mentioned ask is solved using didactic channel allocation algorithm
Topic, the specific implementation process is as follows:
1) input: the data-rate requirements of FUEs areFBSjThe femto user FUE u of service is on subchannel k
Signal to Interference plus Noise Ratio be
2) mean data rate of each cluster is calculated:Cn| for FUEs in n-th of cluster
Number；
3) subchannel number that each cluster needs is determined:
4) each available subchannels are successively calculated in the SINR of each cluster, for example, SINR of the subchannel k in n-th of cluster are as follows:
5) judge that subchannel k is maximum in the SINR of which cluster, it, will be sub if this cluster is not allocated to enough subchannels
Channel k distributes to this cluster；
6) it updatesWith the allocated number of subchannels of each cluster.Repeat step 4) -5) until all
Subchannel has been assigned.
303, power distribution is executed to FUEs, concrete function is realized are as follows:
After the completion of the subchannel distribution of FUEs, readjusted using power of the KKT condition to initial mean allocation, with maximum
The power system capacity for changing all clusters is optimization aim, is modeled as follows:
Wherein,It is the gain interference ratio on subchannel k, it should
Value has determined that when executing subchannel distribution to femto user u；For FBSjGeneral power limitation, i.e. constraint condition C1 show
FBSjIt is not more than its general power in the transimission power of all subchannels；RjFor FBSjMinimum-rate demand, i.e. constraint condition C2 table
Bright FBSjThe data rate obtained on all channels will meet its minimum-rate demand；ξk,uIt is femto user u in cluster by it
The interference threshold of his femto user,Respectively indicate femto base station FBSjTo letter of the macro user e on subchannel k
Road gain, then constraint condition C3 shows that femto user u is met its interference threshold by the interference value of other femtos user；ξk,m
It is femto user u by the interference threshold of macro user,Respectively indicate femto base station FBSjTo macro user m in subchannel k
On channel gain, then constraint condition C4 shows the interference value of the macro user that femto user u is subject to no more than its threshold value.
According to the object of planning (12) and constraint condition (13), obtained by KKT condition:
Wherein, α, β, θ and ε are Lagrange multipliers, are constant, by above-mentioned KKT equation (14) to transmission powerIt asks
Local derviation is solved, i.e.,It can obtain:
Wherein, For water line.
Beneficial effect to illustrate the invention, the channel model that the present invention uses mainly consider path loss, wall penetration loss,
Shadow fading and antenna gain, design parameter are emulated according to table 1.
1 simulation parameter of table
All FBSs work in closed mode in emulation, i.e., only authorized user are allowed to access.The present invention analyzes institute
The multinomial performance for mentioning algorithm, between outage probability, MUEs average throughput, the spectrum efficiency of Femtocell, FUEs including MUEs
Fairness, the satisfaction of FUEs.
Fig. 4 shows the outage probability of the MUEs in different chamber under MUEs ratio.In emulation experiment, interference threshold is set
Value is -6dB, if practical Signal to Interference plus Noise Ratio is lower than -6dB, then it is assumed that MUE is interrupted.From fig. 4, it can be seen that ungrouped RRA
The MUE outage probability that algorithm obtains can constantly rise with the increase of indoor MUEs ratio, until close to 100%；But this
It invents the MUE outage probability that mentioned algorithm obtains and is always held at 10% or less.Therefore, the mentioned algorithm of the present invention effectively reduces
Interference of the FBSs to MUEs meets the QoS demand of MUEs so that MUEs can satisfy its minimum SINR demand.
Fig. 5, which is described, to be gulped down under different Femto cell deployment densities by being averaged for indoor MUEs that various algorithms obtain
The amount of spitting.Maximum average throughput can wherein be obtained in max carrier to interference theory of algorithm, but this algorithm does not account between MUEs
Fairness, the MUEs that may result in bad channel quality may be distributed less than channel.The mentioned algorithm of the present invention is in FBSs sub-clustering
On the basis of for FUEs distribute subchannel and power, user fairness has been combined, so that the MUEs of bad channel quality can also be obtained
Preferable communication quality is obtained, significantly reduces interference of the FBSs to MUEs, and then improve system performance.
Fig. 6 shows the frequency spectrum service efficiency of the Femtocell under different FBSs density conditions.GASA is dynamically to FBSs
Sub-clustering is carried out, same layer interference that can effectively in elimination system, so that spectrum efficiency is substantially improved.Orthogonal grouping algorithm in group
Opposite mentioned algorithm spectrum efficiency herein is lower, and reason is after the algorithm sub-clustering that FBSs number differs greatly in each cluster,
But the frequency band size of each cluster distribution be it is identical, this has resulted in availability of frequency spectrum reduction.However, proposed by the present invention
GASA-HK algorithm is to have carried out subchannel distribution and power distribution on the basis of GASA algorithm, is executing son letter to FUEs
It can be further reduced interference on the basis of FBSs sub-clustering when road distribution and power distribution, improve the Signal to Interference plus Noise Ratio of FBSs, i.e.,
Improve the system performance of FBSs.
Fig. 7 describes the fairness between FUEs.As Femto cell density improves, RRA algorithm fairness is higher than other
Compare algorithm, but since its randomness interferes FUEs by biggish same layer, so its
Fairness is lower between femto user；The FBSs number of each cluster is unbalanced in orthogonal grouping algorithm between group, causes in different clusters
The interference difference that is subject to of FUEs it is larger；The FUEs fairness that GASA algorithm obtains is substantially better than other algorithms.However, this paper institute
It mentions GASA-HK algorithm and compares GASA algorithm, channel distribution has been carried out to system based on Max-min fairness after sub-clustering
And power distribution, the power of the lower subchannel of Signal to Interference plus Noise Ratio is promoted, while reducing the power of the excessively high subchannel of Signal to Interference plus Noise Ratio,
So that the fairness of FUEs is further promoted.
Fig. 8 describes the satisfaction of FUEs.The mentioned cluster algorithm of the present invention is an iteration searching process, is calculated in conjunction with heredity
The advantages of method and simulated annealing, the FBSs number that can be adaptively adjusted according to FBSs deployment density in each cluster, point
Cluster performance is continuously improved, and can preferably eliminate interference, and compare other algorithms, and the mentioned algorithm of the present invention can make the satisfaction of FUEs
Degree is maintained at a higher level.Further, GASA-HK algorithm carries out power adjustment on the basis of GASA algorithm, so that more
More FUEs are able to satisfy rate requirement.
Claims (7)
1. a kind of resource allocation methods for Femtocell double-layer network based on sub-clustering, which is characterized in that including following step
It is rapid:
Step 101: subchannel distribution being executed to macro user MUEs using three-wheel subchannel distribution algorithm；
Step 102: the object of planning and constraint condition distributed according to macro user power use classical water-filling algorithm for MUEs distribution
Power；
Step 103: using improved Global Genetic Simulated Annealing Algorithm GASA for Femto cell sub-clustering；
Step 104: according to the rate requirement of femto user FUEs, heuritic approach being used to distribute subchannel for FUEs；
Step 105: needing-Ku En-Plutarch KKT condition to carry out power distribution to femto user FUEs using Caro；
The step 103 uses the improved Global Genetic Simulated Annealing Algorithm GASA to include: for Femto cell sub-clustering
Using the interference summation minimum between the femto base station FBSs in same cluster as objective function, modeling optimization equation:And meet constraint condition:Cg∩Cn=Φ (g, n ∈ χ, g ≠ n)
And xin∈{0,1}；Wherein, χ={ 1 ..., NAIndicate the set of cluster, F and NARespectively indicate the quantity of femto base station FBSs
With the quantity of cluster；wijIt is FBSiAnd FBSjBetween interference weight；xinIt is the sub-clustering oriental matrix of FBSs, works as xinWhen=1, indicating will
FBSiN-th of cluster is assigned to, x is worked asinWhen=0, i.e. expression FBSiN-th of cluster is not assigned to；CnIndicate the set of FBSs in n-th of cluster, Cg
Indicate the set of FBSs in g-th of cluster,For the set of FBSs total in system,
This sub-clustering is solved the problems, such as using Global Genetic Simulated Annealing Algorithm, comprising:
103A: initialization: population at individual size sizepop, maximum evolution number MAXGEN, crossover probability Pc, mutation probability Pm,
Anneal initial temperature T0, temperature cooling ratio k, final temperature Tend；
103B: it is random to generate initial population Chrom, calculate fitness value f individual in populationi, wherein setting fiTo plan mesh
Mark；
103C: the genetic manipulations such as selection, intersection and variation are implemented to population Chrom, that is, generate new FBS sub-clustering result；To production
Raw new individual calculates its fitness value fi', if fi′<fi, then old individual is replaced with new individual, otherwise, with probability exp ((fi-
fi')/T) receive new individual；
103D: if evolution number gen < MAXGEN, gen=gen+1, go to step 103C, otherwise, step 103E is gone to；
103E: if Ti<Tend, then algorithm terminates, and returns to globally optimal solution, otherwise, executes cooling operation Ti+1=kTi, go to step
103B。
2. the resource allocation methods according to claim 1 based on sub-clustering, which is characterized in that the step 101 utilizes three
It includes: that reference shannon formula models macro user m data speed that wheel channel allocation algorithm, which executes subchannel distribution to macro user MUEs,
The more new formula of rate isWherein, M is macro total number of users, and K is subchannel sum,For Signal to Interference plus Noise Ratio of the macro user m on subchannel k, Δ f is channel width；And then consider that the data rate of macro user is asked
It asks, is that macro user distributes subchannel under the premise of meeting macro user rate section.
3. the resource allocation methods according to claim 2 based on sub-clustering, which is characterized in that described to meet macro user's speed
It is that macro user distributes subchannel under the premise of rate section, comprising:
Step 101A: traversing all subchannels, finds out the subchannel k for enabling macro user m to obtain maximum channel gain, and by sub- letter
Road k distributes to macro user m, if the rate of obtained macro user m meets its minimum speed limit demand, macro user m no longer participates in letter
Road distribution, and then if all macro user data rates being calculated are all satisfied minimum speed limit demand, exit circulation；
Step 101B: if subchannel has residue, repeat step 101A, macro user rate Rule of judgment becomes judgement meter
Whether the rate of obtained corresponding macro user meets its flank speed demand；
Step 101C: if subchannel still has residue, repeating step 101A, no longer carries out macro user data rate judgement.
4. the resource allocation methods according to claim 1 based on sub-clustering, feature exist, the step 102 is according to macro use
The object of planning and constraint condition of family power distribution, use the classical water-filling algorithm to include: for MUEs distribution power
To maximize power system capacity as optimization aim, maximum general power is constraint condition, constructs the power distribution target letter of MUEs
Number:And meet constraint condition:
It uses water-filling algorithm for macro user's distribution power, obtainsWherein,η
=Δ f/ ζ ln2 is water line；It is the gain interference ratio on subchannel k,
Indicate channel gain of the macro base station to macro user m on subchannel k,Indicate femto base station FBSjExist to macro user m
Channel gain on subchannel k；Indicate femto base station FBSjTransmission power on subchannel k；σ2For noise function
Rate；ζ is Lagrange multiplier, is constant；For transmission power of the macro base station on subchannel k,For total transmitting function
Rate, Δ f are channel width, and M is macro total number of users, and K is subchannel sum.
5. the resource allocation methods according to claim 1 based on sub-clustering, which is characterized in that the step 104 is according to milli
The rate requirement of pico- user FUEs, using heuritic approach to distribute subchannel for FUEs includes: the data to maximize FUEs
Rate is optimization aim:And meet constraint conditionWherein, DjFor FBSjService
FUEs set；Δ f is channel width；For FBSjSignal to Interference plus Noise Ratio of the femto user u of service on subchannel k；
For FBSjThe rate requirement of the femto user u of service；If ak,n=1, indicate that subchannel k distributes to cluster Cn, otherwise, ak,n=0；K
For subchannel sum.
6. according to claim 5 based on the resource allocation methods of sub-clustering, which is characterized in that the step 105 utilizes KKT item
It includes: to establish function model by optimization aim of maximize handling capacity that part, which carries out power distribution to femto user FUEs:
And
Wherein,Indicate FBSjTo the transmission power of user u on subchannel k,Indicate MUEs
Set, It is the gain interference on subchannel k
Than, when femto user u is in subchannel distribution it has been determined thatFor FBSjChannel gain on subchannel k,WithRespectively FBSiWith macro base station to the channel gain of femto user u,WithRespectively FBSiBelieving with macro base station
Transmission power on road k, σ2For noise power；
In constraint condition C1,For FBSjTotal transmission power, then C1 indicates FBSjTransmission power in all subchannels it
Be not more than FBSjTotal transmission power；In constraint condition C2, RjFor FBSjMinimum-rate demand, then C2 indicate FBSjInstitute
There is transmission rate in subchannel and is not less than its minimum-rate demand；In constraint condition C3, ξk,eIndicate femto user in cluster
U by other femtos user interference threshold,Respectively indicate femto base station FBSjTo macro user e on subchannel k
Channel gain, then C3 indicate FBSjThe femto user u serviced is by FBSjThe interference summation of the other users serviced is little
In the interference threshold of femto user u；In constraint condition C4, ξk,mIt is femto user u by the interference threshold of macro user,Respectively indicate femto base station FBSjTo channel gain of the macro user m on subchannel k, then C4 indicates FBSjIt is taken
The interference threshold that the femto user u of business is interfered summation to be not more than femto user u by macro user.
7. the resource allocation methods according to claim 6 based on sub-clustering, which is characterized in that described to use KKT condition pair
FUEs carries out power distribution: according to the optimization object function and constraint condition of FUEs power distribution, quoting KKT item
Part obtains:
Wherein,For water line, α, β, θ and ε are Lagrange multipliers, are
Constant；
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