CN109450494A - Heterogeneous network channel and power resource combined distributing method based on CoMP - Google Patents
Heterogeneous network channel and power resource combined distributing method based on CoMP Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/022—Site diversity; Macro-diversity
- H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L5/00—Arrangements affording multiple use of the transmission path
- H04L5/003—Arrangements for allocating sub-channels of the transmission path
- H04L5/0058—Allocation criteria
- H04L5/0071—Allocation based on fairness other than the proportional kind
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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Abstract
Present applicant proposes a kind of heterogeneous network channel and power resource combined distributing method based on CoMP, wherein the above method includes: building heterogeneous network, and CoMP joint transmission mode is supported in each layer base station in the heterogeneous network；The mathematical model of heterogeneous network channel and power resource allocation is established based on constraint condition: ensuring that each base station distributes to the maximum transmission power limitation of the transimission power summation of each channel without departing from the base station and guarantees that every channel at most distributes to a user；Based on the mathematical model, channel distribution vector is determinedWith the power allocation vector of each base stationTo realize that the handling capacity of worst user reaches maximization.The application can take into account the Resource co-allocation of system performance and user fairness.
Description
[technical field]
The application more particularly to a kind of heterogeneous network channel and power resource combined distributing method based on CoMP.
[background technique]
Nearly ten years, explosive growth is presented in the data traffic of wireless communication system.It is expected that 2010 to 2020, the whole world is moved
Dynamic data traffic will grow beyond 100 times.Therefore, next-generation 5G mobile communications network requires theoretical peak transmission rate that can reach
To 10Gb or more, the network transmission speed (20~100Mbps) than current 4G is higher by hundreds times.And traditional single layer wireless network
Access technology has been approached theoretical limit, is unable to satisfy demand.Therefore, the isomery of multitiered network superposition covering is realized in areal
Network becomes the important selection of 5G network, and network deployment will show the development trend of densification.
By increasing the low-power such as cell (small cell) base station, relaying or spaced antenna in traditional macrocell
Node, heterogeneous network shortens the signal transmission distance of base station and terminal room, enhances received signal strength, and then improves and be
System capacity.It realizes that multilayer covering has effectively shared network load in hot zones, brings better internet to experience to user.But
Meanwhile in order to efficiently utilize frequency spectrum resource, macrocell and cell usually share same frequency spectrum resource.Therefore, interlayer interference is
Heterogeneous network technologies develop the bottleneck problem faced.
Cooperative multi-point (CoMP) technology is the Release 11 and a key technology in version later of LTE-A evolution,
It is intended to suppress or eliminate the interference of minizone, promotes Cell Edge User service quality.The base station of CoMP technology is supported to pass through back
The information such as journey link switching user and channel status, combined dispatching system resource, so that the co-channel signal from other base stations
Severe jamming will not be caused to user, it might even be possible to become useful signal.It has been obtained in traditional macrocell network adequately
Research and application, it was demonstrated that its validity in terms of solving cell interference issues.Therefore, CoMP is introduced into heterogeneous network to press down
Interlayer interference processed, lifting system performance become the new research hotspot in heterogeneous network field.
[application content]
The embodiment of the present application provides a kind of heterogeneous network channel and power resource combined distributing method based on CoMP, with
Fairness while realizing lifting system handling capacity between protection user.
Heterogeneous network channel and power resource combined distributing method of the application based on CoMP, comprising:
Heterogeneous network is constructed, CoMP joint transmission mode is supported in each layer base station in the heterogeneous network；
Target is turned to realize that the handling capacity of worst user reaches maximum, establishes the channel and power money of the heterogeneous network
The mathematical model of source distribution, the mathematical model indicate are as follows:
Wherein, the constraint condition of the mathematical model is including: ensuring that the transimission power of each channel is distributed in each base station
Summation is limited without departing from the maximum transmission power of the base station, and formula indicates are as follows: subject toAnd guarantee that every channel at most distributes to a user, formula indicates are as follows:
Based on channel and power resource co-allocation algorithm, channel distribution vector is calculatedWith each base station
Power allocation vector
Further, the heterogeneous network is by a macro base station in network central and the macro base station coverage area
The small base stations composition of B, wherein the small base station type is one of base station micro, pico and femto or combination.
Further, the channel and power resource co-allocation algorithm include:
Step 301, the number of iterations serial number: t=1, maximum number of iterations t is setmax；Set initial power:Set multiplier μ, υ, β, step-length α, λ, error threshold ε；
Step 302, t iteration is carried out, optimum channel allocation vector ρ (t) is asked based on allocation vector algorithm；Based on more base stations
Power joint allocation algorithm seeks optimal power contribution vector p (t)；Multiplier λ (t), μ (t), α (t) and β (t) are updated respectively；
Step 303, return step 302, until | | μ (t+1)-μ (t) | |2< ε and | | β (t+1)-β (t) | |2< ε or t=
tmax；
Step 304, channel distribution vector ρ and power allocation vector p is exported.
Further, the allocation vector algorithm includes:
Step 401, channel set φ={ 1,2 ..., C }；
Set initial channel allocation of parametersUser's accumulation rate
Calculate user's obtainable transmission rate on each channel
Step 402, it iterates to calculate: finding the smallest user u of transmission rate*, i.e.,Finding can use
Family u* obtains the channel c of peak transfer rate*, i.e.,Signaling channel distributes variableUpdate channel set
Close φ=φ-{ c*, user rate
Step 403, return step 402, until
Step 404, channel distribution vector ρ is exported.
Further, more base station power co-allocation algorithms include:
Step 501, the number of iterations serial number: k=1 is set；Initial power allocation vector p (0) is set as by channel and power
Resource co-allocation algorithm p obtained (t-1)；
Step 502, kth time iteration: to all base station b, 0≤b≤B；It is based onSeek GcWithIt calculates
1/μb: to any c, askAnd it sorts in descending order；J is enabled to successively decrease from C to 1
IfThen stop；
Based on formulaWherein, []+=max (0), is asked
Step 503, worst user rate is soughtK=k+1；Return step 502, until rminIt receives
It holds back；
Step 504, output power allocation vector p.
In above technical scheme, to maximize worst user throughput as target founding mathematical models, and then base is devised
In the iterative algorithm that Lagrange duality decomposes.It is 4 subproblems by former PROBLEM DECOMPOSITION, to reduce in iteration each time
The computation complexity of algorithm, and separately designed channel allocation algorithm and the power distribution calculation based on iteration and based on dichotomy
Method.User throughput and in terms of be better than other existing methods, and iterative algorithm can be in less iteration time
Convergence is realized in number.
[Detailed description of the invention]
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, the drawings in the following description are only some examples of the present application, for this field
For those of ordinary skill, without creative efforts, it can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is that the Mean Speed of the application user compares；
Fig. 2 is that the application user's Mean Speed normalizes Multilayer networks；
Fig. 3 is comparative analysis when the worst rate of the application user is long, U=30；
Fig. 4 is the worst user rate under the application different user density scene；
Fig. 5 is the worst rate of user under the application different base station density, U=30；
Fig. 6 is that the application justice sex index compares；
Fig. 7 is the comparison of the application subscriber channel maximum scheduling time inter；
Fig. 8 is the more base station power co-allocation convergences of the application；
Fig. 9 is the application power resource co-allocation convergence；
Figure 10 is that the application channel distribution and simple algorithm justice sex index compare；
Figure 11 is power distribution algorithm of the application in iteration and the power distribution algorithm multiplier convergence based on dichotomy
Situation compares.
[specific embodiment]
In order to better understand the technical solution of the application, the embodiment of the present application is retouched in detail with reference to the accompanying drawing
It states.
It will be appreciated that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.Base
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
Its embodiment, shall fall in the protection scope of this application.
The term used in the embodiment of the present application is only to be not intended to be limiting merely for for the purpose of describing particular embodiments
The application.In the embodiment of the present application and the "an" of singular used in the attached claims, " described " and "the"
It is also intended to including most forms, unless the context clearly indicates other meaning.
Heterogeneous network is by B small bases in a macro base station (base station macro) and its coverage area in network central
It stands composition.Small base station can be the multiple types such as the base station micro, pico and femto.That is, the resource that the application proposes
Allocation plan is suitable for multilayer heterogeneous network.Enable macro base station serial number 0,1≤b of each small base station serial number≤B.In joint transmission mould
Under formula, macro base station and each small base station collaboration provide data transport service to U user.For base station b (b ∈ 0,1 ...,
B }) maximum transmission power.
It is shared by macro base station and small base station if system shares C channel.Define channel distribution variable
Indicate that channel c (c ∈ { 1 ..., C }) is allocated to user u (u ∈ { 1 ..., U }), it is on the contraryFor
Cochannel in cell is avoided to interfere, every channel at most distributes to a user simultaneously.IfIt is distributed on channel c for base station b
Transimission power, ifAndIndicate that base station b will transmit data to user u on channel c.Pass through the application's
Resource co-allocation scheme can dynamically determine the serving BS of user uRather than as existing some sides
Case preassigns the serving BS of each user, and remains unchanged in entire resource allocation process.This Dynamic Assignment
Resource can be distributed more flexiblely, improve system spectral efficiency.
It enablesIndicate the wireless link channel gain on channel c, from base station b to user u.Assuming that each base station can and
When obtain the accurate channel state information of user (CSI).
Under CoMP joint transmission mode, user u receives signal, the signal-to-noise ratio obtained on channel c from each cooperative base station
(SNR) it is represented by
Wherein, N0For additive white Gaussian noise power.On channel c, peak transfer rate obtained by user u are as follows:
Wherein, Δ f is channel width.Therefore, in first resource scheduling, the maximum throughput that user u can be obtained can
It indicates are as follows:
The application is by determining optimal channel distribution vectorWith the power allocation vector of each base stationThe handling capacity of worst user is set to reach maximization, to protect user while lifting system handling capacity
Between fairness.
Heterogeneous network channel and power resource combined distributing method one preferred embodiment of the application based on CoMP, comprising:
Heterogeneous network is constructed, CoMP joint transmission mode is supported in each layer base station in the heterogeneous network；
Target is turned to realize that the handling capacity of worst user reaches maximum, establishes following mathematical model:
Wherein, constraint condition (4a) ensures that the transimission power summation of each channel is distributed to without departing from the base in each base station
The maximum transmission power limitation stood, constraint condition (4c) guarantee that every channel at most distributes to a user.
The application introduces a new variable first in objective functionSimplify objective function, while by channel distribution
The 0-1 constraint relaxation of variable is section [0,1], and (4) equivalence is rewritten as
Revised problem (7) exists simultaneously integer variable (channel although being reduced to single maximization problems
Distribute variable) and non-integer variable (power distribution variable), it is still that the very high non-linear mixing of computation complexity is whole
Number planning problem.
For the algorithm for finding a kind of fast convergence, the application uses Lagrange duality decomposition method to carry out loose constraint item first
Part reduces problem complexity.To formula (7), its Lagrangian formula is listed:
Wherein, μ={ μ0,...,μB, υ={ υ1,...,υCAnd β={ β1,...,βUIt is to correspond respectively to constraint condition
The Lagrange multiplier of (7a), (7c) and (7e).So far, Lagrange duality function can be expressed as
By Duality Decomposition, can be maximized at given μ, υ and βAcquire former problem most
Best resource distribution solution.By Lagrange duality method, problem (9) reduces constraint condition compared to problem (7), but problem (9)
It solves and needs while determining resource allocation vector and Lagrange multiplier, computation complexity still with higher.
For this purpose, former Resource co-allocation PROBLEM DECOMPOSITION is further 4 subproblems by the application: channel distribution, power point
Match,Selection and multiplier update.Subproblem is solved using iterative method respectively, until algorithmic statement.
Detailed step is shown in algorithm 1: channel and power resource co-allocation algorithm.
Algorithm 1: channel and power resource co-allocation algorithm
Step 101, it initializes:
Set the number of iterations serial number: t=1, maximum number of iterations tmax。
Set initial power:
Set multiplier μ, υ, β, step-length α, λ, error threshold ε.
Step 102, the t times iteration:
Optimum channel allocation vector ρ (t) is sought based on channel allocation algorithm (algorithm 2).
Algorithm 3: more base station power co-allocation algorithms
Optimal power contribution vector p (t) is sought based on more base station power co-allocation algorithms (algorithm 3).
Multiplier λ (t), μ (t), α (t) and β (t) are updated respectively.
Step 103, return step 102, until | | μ (t+1)-μ (t) | |2< ε and | | β (t+1)-β (t) | |2< ε or t=
tmax。
Step 104, channel distribution vector ρ and power allocation vector p is exported.
It enablesIndicate the locally optimal solution of the channel distribution vector under power allocation vector known case.According to most
The KKT condition of excellent solution, available following relationship:
For channel distribution vector, only 0,1 two kind of value.WhenWhen, have
I.e.WhenWhen, have
I.e.The comprehensive available expression formula of both of these case
Wherein,
Since the constraint condition (4c) of channel distribution variable is relaxed in formula (8), the channel that formula (13) obtains divides
With scheme it cannot be guaranteed that every channel is at most assigned to only a user.Therefore, some documents based on Lagrangian Arithmetic are adopted
It uses H maximum value as method for channel allocation [], but does not ensure that acquisition locally optimal solution in this way.For this purpose, the application is based on
Max min thought devises channel allocation algorithm (see algorithm 2).
As shown in the step 102 of algorithm 1, in the t times iteration, algorithm 2: channel allocation algorithm will be changed based on the last time
The optimal power contribution vector p (t-1) that generation obtains calculates the locally optimal solution of present channel distribution.Core concept is: will most
Good channel (message transmission rate that user can obtain on this channel is maximum) distributes to the smallest user of accumulation rate, directly
It is finished to all channel distributions.
Algorithm 2: channel allocation algorithm
Step 201, it initializes:
Channel set φ=1,2 ..., C }.
Set initial channel allocation of parametersUser's accumulation rate
Calculate user's obtainable transmission rate on each channel
Step 202, iteration:
Find the smallest user u of transmission rate*, i.e.,
User u can be made by finding*Obtain the channel c of peak transfer rate*, i.e.,
Signaling channel distributes variable
Update channel set φ=φ-c *, user rate
Step 203, return step 202, until
Step 204, channel distribution vector ρ is exported.
It enablesFor the power allocation vector locally optimal solution under channel distribution vector ρ known case, according to optimal
The KKT condition of solution, available following relationship:
Based on algorithm 2, any channel c has been allocated to user u known to the applicationc, therefore (15) can be reduced to
WhenWhen, have
I.e.
In conjunction withThe case where, and enableWithPower distribution can be become
Amount expression are as follows:
Wherein, []+=max (0).
Although formula (19) and the solution of water flood have similar form,In includeIt is the function of other cooperative base stations
Rate distribute variable, need andIt determines together.So formula (19) cannot directly find out the power distribution solution of all base stations.For this purpose,
This section proposes more base station power co-allocation algorithms (algorithm 3) based on iteration.The algorithm is based on previous iteration in algorithm 1 and obtains
The optimal power contribution vector p (t-1) and Lagrange multiplier β (t-1) obtained, asks the part of current power allocation vector p (t)
Optimal solution.Basic ideas are: in kth time iteration, fixing other base stationsPower assignment value, successively calculate each
1/ μ of water level line of base station bb, and find out according to formula (19) the current power allocation vector of the base stationIt is based on
Current power allocation vector p (k) updates worst user rate and enters next iteration, until algorithmic statement.
Algorithm 3: more base station power co-allocation algorithms
Step 301, it initializes:
Set the number of iterations serial number: k=1.
Initial power allocation vector p (0) is set as by 1 p obtained (t-1) of algorithm.
Step 302, kth time iteration:
To all base station b, 0≤b≤B
It is based onSeek GcWith
Calculate 1/ μb:
To any c, askAnd it sorts in descending order.
J is enabled to successively decrease from C to 1
IfThen stop.
Based on formula (19), ask
Step 302, worst user rate is sought
Return step 302, until rminConvergence.
Step 304, output power allocation vector p.
Algorithm 1: channel and power resource co-allocation algorithm by base station resource manager operation.If LTE system, base
The each TTI of resource manager of standing (1ms duration) executes the algorithm when starting.The channel that user reported in last TTI is called in base station
ConditionAnd set algorithm need some parameter μs, υ, β, the initial value of α, λ, ε, run algorithm 1, obtain channel distribution to
ρ, p are measured, this is final output.Radio resource (channel resource and power resource of base station) distribution is exactly to determine the two ginsengs
Number, base station knows that the two parameters are known that in this TTI which channel gives which user, that is, passes which user's
Data are transmitted using great power.
Algorithm 1: fair resource allocation may be implemented (by maxmin maximum in channel and power resource co-allocation algorithm
Change worst user rate target), the transmission rate that each user obtains is close.This is in Fig. 1 this it appears that coming, user's speed
Rate is closest.One angle of radio resource allocation algorithm is that operator wishes that overall throughput is big, another angle is that user is uncommon
It is fair to hope.But channel and power resource are limited, and two angles have conflict.But our algorithm target is that maximization is worst,
Fair and throughput of system can be taken into account.Fig. 1 illustrates user fairness, Fig. 2 customer center rate highest, and concentrates very much, says
Bright user's overall rate is all higher, and system overall throughput is relatively good
It calls algorithm 2 to seek channel distribution vector ρ (t) in algorithm 1, algorithm 3 is called to seek optimal power contribution vector p (t),
The two vectors are constantly updated in algorithm 1, to the last converge on final ρ, p, are exactly output valve.Resource allocation in a base station
It will very quickly (ms grades).What the application finally called is derived as a result, making there are also the big problem decomposition approach used is derived
Algorithm it is very simple, be for base station limited for computing capability in this way it is good, do not need big memory and power, Fig. 8 is arrived
Figure 11 illustrates the application algorithm, and iteration can restrain several times, that is, algorithm 1 can export as long as several times as long as calling algorithm 2 and 3
End value.If solving the optimization problem of max min with these tools of MATLAB, a result is not run not Chu Lai within one day yet.
Embodiment is expanded, algorithm 4 is another power distribution algorithm for replacing algorithm 3.In wireless network resource distribution,
Dichotomy is also often used to calculate power distribution problems.Power distribution subproblem is solved using dichotomy, is shown in algorithm 4.Pass through
Parser process, it can be seen that the main distinction of algorithm 3 and algorithm 4 is that the update mode of multiplier μ is different.Algorithm 3 first can by μ
The value of energy arranges in descending order, then selects suitable water level line, that is, suitable μ value, calculates function further according to formula (19)
Rate distribution solution.And algorithm 4 first passes through dichotomy and obtains μ value, updates power distribution solution further according to formula (19), finally according to constraint item
Part updates the bound of μ, the calculating for μ value in next iteration.For the bound of μ, different initial values is set in very
The convergence rate of algorithm 4 is influenced whether in big degree, and the maximum number of iterations upper limit of algorithm 3 is subchannel number C, because
This, the algorithm 4 based on dichotomy has bigger uncertainty.
Algorithm 4: the power distribution algorithm based on dichotomy
Step 401, it initializes:
The number of iterations serial number l=1 is set, error threshold ε is set.
Initial power allocation vector p (0) is set as by algorithm 4-1 p obtained (t-1).
Step 402, the l times iteration:
Set the upper limit μ of multiplier μmaxWith lower limit μmin。
To all base station b, 0≤b≤B
WhenShi Zhihang or less is operated:
Based on formula (19), ask
IfThen updateOtherwise it updates
Step 403, worst user rate is sought
Return step 402, until rminConvergence.
Step 404, output power allocation vector p.
After solving channel distribution subproblem and power distribution subproblem, the application can be bright to glug shown in (9)
Day dual function is simplified, make to leave behind in objective function and constraint condition withRelevant part, and it is indicated again
Are as follows:
It enablesFor the optimal solution of problem (20).It is required that the maximum value of objective function, can discuss in two kinds of situation.WhenWhen, it is contemplated thatIt is nonnegative number, makesValue it is maximum,0 should be equal to；When
When,The maximum value within the scope of its should be taken, i.e.,In conjunction with both of these case, and enableIt is availableOptimal selection scheme:
According to formula (13) and (19), channel distribution and power distribution are completed, needs to know Lagrange multiplier μ, υ and β
Value.
The value of multiplier is updated using subgradient algorithm.Firstly, the dual problem of problem (9) is expressed as
min D(μ,υ,β) (23)
subject to μ≥0,υ≥0,β≥0
And then the subgradient expression formula of available D (μ, υ, β):
Since every channel finally can all distribute to some user, and can only at most be distributed in first resource scheduling
One user, i.e.,Any channel c (c ∈ { 1 ..., C }) is set up always.Therefore,Perseverance is set up, only
It needs to do multiplier update to μ and β.Update method is as follows:
μb(t+1)=[μb(t)-λ(t)Δμb(t)]+ (25a)
βu(t+1)=[βu(t)-α(t)Δβu(t)]+ (25b)
Wherein, λ (t) and α (t) is the update step-length of multiplier μ and β respectively.
The application verifies the scheme of proposition and the performance of algorithm in the heterogeneous network downlink based on LTE.Son
Channel number C=50, subchannel bandwidth Δ f=180kHz, the maximum transmission power of macro base stationLarge scale declines
The calculating fallen follows L=122.85+34.88log10(d), wherein d indicates base station to the distance of user, and unit is km.Small scale
Fading model is normalized Rayleigh fading, and Carrier To Noise Power Density is -174dBm/Hz.In addition, obeying the yin of logarithm normal distribution
The mean square deviation of shadow decline is 10dB, penetration loss 20dB.6 users and 12 are uniformly distributed in the coverage area of macrocell
Flow hot spot region, each hot zone are uniformly distributed 2 users.
For the channel and power resource co-allocation scheme (being abbreviated as JSPA herein) for verifying the application proposition, the application will
It has carried out simulation comparison with other four kinds typical heterogeneous network Resource Allocation Formulas.1. these four reference schemes are respectively as follows:
Classical polling scheme (RR), every channel are sequentially allocated by serial number to each user, and the transimission power of each base station is in all letters
Mean allocation on road；2. using maximum system throughput as the channel of target and power joint Resource Allocation Formula；3. based on tired
The scheduling scheme (CDFS) for counting distribution function carries out channel distribution according to the cumulative distribution function of user rate, and function is transmitted in base station
Rate mean allocation on all channels；4. Proportional Fair scheme (PFS) has been taken into account public between throughput of system and user
The Resource Allocation Formula of levelling.
The application will compare analysis to these types of scheme under macro-femto heterogeneous network scene.Femto base
It stands maximum transmission power
Fig. 1 is the average transmission rate that each user measured obtains in 50 TTI.By user according to respective average speed
The arrangement of rate ascending order.From the results, it was seen that JSPA algorithm each user's Mean Speed obtained that the application proposes is very close,
A certain distance is showed between the highest and lowest Mean Speed that RR and PFS is obtained, substantially difference 6Mbps or so.And TMS and
Under CDFS two schemes, although user's highest average rate value is much larger than 20Mbps, there are certain customers to fail acquisition resource (flat
Equal rate is close to 0Mbps), and the speed difference between user is away from clearly.
The normalization Multilayer networks figure of user's Mean Speed under different schemes is given in Fig. 2, from another angle
Result is analyzed.Firstly, from the figure, it can be seen that the Mean Speed distribution of TMS and CDFS two schemes is most wide
(very small part of 0-20Mbps is only indicated in figure), followed by RR and PFS.The Mean Speed distribution that JSPA scheme obtains
Range is minimum, and the Mean Speed size for indicating that user obtains is closest.On the other hand, Cong Tuzhong can also observe probability density
Mean Speed size corresponding to maximum point, that is, the Mean Speed that most users can obtain, referred to as collection middling speed
Rate.It can be seen that the Mean Speed of most users is all 0Mbps in TMS and CDFS.The collection medium-rate of RR and PFS is distinguished
It is relatively high for 11Mbps and 12Mbps or so, but it is still below the 13Mbps of JSPA scheme.Fig. 1 and Fig. 2 illustrates that JSPA scheme obtains
To user's Mean Speed distribution be to concentrate the most, that is, the Mean Speed difference between each user is minimum, Er Qiefang
Case JSPA obtains concentrating rate value maximum.Illustrate JSPA scheme that the application proposes may be implemented user throughput and fairness it
Between balance.
Next as 30, as a result situation of change of the observation worst rate of user when long in scheduling process sets number of users
As Fig. 3 is indicated.It can be seen that the worst Mean Speed of TMS and CDFS are 0Mbps always during entire resource allocation,
The worst Mean Speed of RR and PFS floats up and down, but generally speaking the worst Mean Speed of RR is higher than PFS.And JSPA
The worst Mean Speed that scheme obtains then shows the trend being incremented by with TTI, and is consistently higher than other four kinds of schemes.Thus may be used
To be concluded that when long in scheduling process, compared to other four kinds of reference schemes, the JSPA scheme that the application proposes can
To obtain maximum worst Mean Speed, the target of design of scheme is realized.
In order to sufficiently verify the performance of JSPA scheme, this trifle set different user density, different base station density it is more
Kind simulating scenes are to simulate the various situations in reality.It compared the average speed of worst user that each scheme obtains under various scenes
As a result rate is shown in Fig. 4 and Fig. 5.Each point is the speed average value that each user obtains in 50 TTI in figure.It can from figure
To find out, the worst Mean Speed of TMS and CDFS are all 0Mbps under any scene.The worst average speed of JSPA, RR and PFS
Rate all shows downward trend with increasing for number of users, because with the increase of user, the number of channel of each user's acquisition
It reduces.But either channel number is less than still under opposite senses in number of users, the worst Mean Speed that JSPA is obtained
Consistently greater than other four kinds of schemes.From fig. 5, it can be seen that the worst Mean Speed of tri- kinds of schemes of JSPA, RR and PFS is all with small
Increasing for number of base stations and become larger, and the worst rate of JSPA is consistently higher than other schemes.To sum up, compared to it
His four kinds of schemes, JSPA scheme under various scenes worst user's Mean Speed can be realized maximize.
For the fairness for intuitively measuring scheme, when the application calculates long in scheduling user rate fair sex index:
Wherein, RuIndicate the Mean Speed of user u in current TTI, the value of fair sex index J is bigger, indicates that user is average
Fairness between rate is higher.The case where fair sex index that Fig. 6 show user's Mean Speed changes with TTI.At three kinds
It considers in the Resource Allocation Formula of fairness, JSPA can obtain always higher fair sex index compared to RR and PFS.And
The fair sex index of JSPA is incremented by with TTI, has just converged to maximum value 1 after about 4 TTI.In other words, JSPA energy
The fairness of user rate is realized within shorter scheduling time.
In addition, also having observed the maximum scheduling interval (MIT) of worst user.Scheduling interval is two sub-distribution before and after user
To the interval duration of resource.The maximum scheduling interval of each user is calculated in 50 TTI, and then takes out the scheduling of worst user
Spacing value, as MIT.Fig. 7 compared the MIT value of different schemes under three kinds of scenes.Since the distribution principle of RR scheme is will to believe
Road is sequentially sequentially allocated to each user, so its scheduling interval is minimum.The application propose JSPA scheme can obtain with
The very close scheduling interval value of RR scheme, is better than other three kinds of schemes.Because scheduling interval can reflect user and be serviced
Time delay, above results proved that family, which can be used, in the JSPA scheme proposed obtains more stable network connection service,
It is transmitted suitable for the application more demanding to delay and delay jitter, such as video flowing.
In simulation process, the performance of the algorithm of the application proposition is also demonstrated.Fig. 8 Fig. 9 is more base station power joints point
With algorithm (algorithm 3) and the iterative channel and power resource co-allocation algorithm (algorithm 1) that are decomposed based on Lagrange duality
Worst user rate with the number of iterations situation of change.It is observed under four kinds of heterogeneous network scenes respectively.Simulation result table
It is illustrated, algorithm 3 and algorithm 1 can realize convergence in less the number of iterations.
Figure 10 carries out channel allocation algorithm 2 that the application is proposed and the simple algorithm after deriving based on Lagrange pair
Than, it can be seen that the channel allocation algorithm of proposition can reach higher fair sex index in shorter resource allocation time, more
The fairness between user is realized well.
Figure 11 is directed to the power distribution algorithm (algorithm 3) based on iteration proposed and the power distribution based on dichotomy
Algorithm (algorithm 4) compared the convergent of multiplier μ under two kinds of algorithms.It can be seen that algorithm 3 is just real after about 8 iteration
Show convergence, and the case where algorithm 4, is more complicated.On the one hand, no matter the initial upper limit μ of μmaxValue be how many, even
In the case where preferably, algorithm 4 is also required to just restrain after at least 12 iteration.On the other hand, if μmaxValue it is different, calculate
Method 4, which reaches the number of iterations that convergence needs, can also show very big difference, and convergence has very big uncertainty.
The foregoing is merely the preferred embodiments of the application, not to limit the application, all essences in the application
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the application protection.
Claims (5)
1. a kind of heterogeneous network channel and power resource combined distributing method based on CoMP characterized by comprising
Heterogeneous network is constructed, CoMP joint transmission mode is supported in each layer base station in the heterogeneous network；
Target is turned to realize that the handling capacity of worst user reaches maximum, establishes the channel and power resource point of the heterogeneous network
The mathematical model matched, the mathematical model indicate are as follows:
Wherein, the constraint condition of the mathematical model is including: ensuring that the transimission power summation of each channel is distributed in each base station
Maximum transmission power without departing from the base station limits, and formula indicates are as follows: subject toAnd guarantee that every channel at most distributes to a user, formula indicates are as follows:
Based on channel and power resource co-allocation algorithm, channel distribution vector is calculatedWith the power of each base station
Allocation vector
2. the method according to claim 1, wherein the heterogeneous network is in the macro base of network central by one
Stand and the macro base station coverage area in the small base stations composition of B, wherein the small base station type is micro, pico and femto
One of base station or combination.
3. the method according to claim 1, wherein the channel and power resource co-allocation algorithm include:
Step 301, the number of iterations serial number: t=1, maximum number of iterations t is setmax；Set initial power:Set multiplier μ, υ, β, step-length α, λ, error threshold ε；
Step 302, t iteration is carried out, optimum channel allocation vector ρ (t) is asked based on allocation vector algorithm；Based on more base station powers
Co-allocation algorithm seeks optimal power contribution vector p (t)；Multiplier λ (t), μ (t), α (t) and β (t) are updated respectively；
Step 303, return step 302, until | | μ (t+1)-μ (t) | |2< ε and | | β (t+1)-β (t) | |2< ε or t=tmax；
Step 304, channel distribution vector ρ and power allocation vector p is exported.
4. the method according to claim 1, wherein the allocation vector algorithm includes:
Step 401, channel set φ={ 1,2 ..., C }；
Set initial channel allocation of parametersUser's accumulation rate
Calculate user's obtainable transmission rate on each channel
Step 402, it iterates to calculate: finding the smallest user u* of transmission rate, i.e.,User u* can be made by finding
The channel c* of peak transfer rate is obtained, i.e.,Signaling channel distributes variableUpdate channel set φ
=φ-{ c*, user rate
Step 403, return step 402, until
Step 404, channel distribution vector ρ is exported.
5. the method according to claim 1, wherein more base station power co-allocation algorithms include:
Step 501, the number of iterations serial number: k=1 is set；Initial power allocation vector p (0) is set as by channel and power resource
Co-allocation algorithm p obtained (t-1)；
Step 502, kth time iteration: to all base station b, 0≤b≤B；It is based onSeek GcWithCalculate 1/ μb:
To any c, askAnd it sorts in descending order；J is enabled to successively decrease from C to 1
IfThen stop；
Based on formulaWherein, []+=max (0), is asked
Step 503, worst user rate is soughtK=k+1；Return step 502, until rminConvergence；
Step 504, output power allocation vector p.
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