CN105898874A - Coordinated multipoint (CoMP) transmission-based distributed heterogeneous network resource distribution method and system - Google Patents

Abstract

The present invention provides a CoMP transmission-based distributed heterogeneous network resource distribution method and system. The beneficial effects of the present invention are that: by setting a sample generation probability according to a pre-estimated user data rate, the convergence speed of an algorithm is accelerated effectively; then an optimal scheduling result satisfying the back-haul capacity limitation is obtained by carrying out the continuous iteration on the generation probability; finally, the algorithm enables the scheduling result to be corrected according to a given data rate threshold value, thereby guaranteeing the minimum data rate requirement of the transmission. By a simulation experiment, the validity and the feasibility at the aspects of improving the system throughput, energy efficiency and user fairness, etc., of a resource distribution algorithm provided by the present invention are verified.

Description

Distributed heterogeneous network resource allocation method based on CoMP transmission and system

Technical field

The present invention relates to communication technical field, particularly relate to distributed heterogeneous Internet resources distribution side based on CoMP transmission Method and system.

Background technology

In order to meet ever-increasing radio communication service demand, 3GPP is at LTE-Advanced (hereinafter referred to as LTE-A) Propose the new network framework model of heterogeneous network (heterogeneous network, HetNet) in the works.HetNet's Core concept is, existing macrocell cover on the basis of, increase polytype low power nodes neatly, as micro, Pico, femto, RRH and relaying etc.^[1].Compared with the macro base station eNodeB (eNB) of LTE-A, these low power consumption node use Through-put power less, dispose flexibly, cost is lower, it is possible to covers the covering blind spot of macrocell, and increases answering of frequency spectrum resource By degree, improve the utilization ratio of resource^[2～7].But, highdensity node deployment can cause minizone co-channel interference intensity Increase.If dealt with improperly, HetNet structure can be made cannot to play in the advantage of the aspects such as spectrum utilization efficiency.In order to solve this Individual problem, LTE-A introduces again coordinated multi-point (coordinatedmultipoint, CoMP) transmission technology^[8～10], to can Effectively transmitted by the cooperation between node, eliminate the co-channel interference of minizone, and increase the money of wireless communication system further Source utilization ratio.

According to the difference of cooperation transmission means, CoMP technology can be further divided into cooperative scheduling/collaborative beam forming (coordinated scheduling/coordinated beamforming, CS/CB) CoMP and Combined Treatment (joint Processing, JP) CoMP two class^[11].Wherein, in CS/CB CoMP transmission, base station is according to the channel specified between user Condition carries out united precoding to the data symbol sent, thus reduces the cochannel interference between neighbor cell.Typical pre- Coding techniques includes dirty paper code (dirty paper coding, DPC)^[12]With linear predictive coding etc..JP CoMP then lays particular emphasis on Active to interference utilizes, it is allowed to the one or more base stations in interference region are same appointment user's service simultaneously.Typically Technology includes that dynamic cell selects (dynamic cell selection, DCS) CoMP and joint transmission (joint Transmission, JT) CoMP.Wherein, JT CoMP technology can not only effectively eliminate co-channel inter-cell interference, additionally it is possible to Utilize these to disturb signal, generate useful signal copy, increase the receiving intensity of useful signal, be that above-mentioned CoMP technology has most One of candidate scheme of potentiality.

Bibliography:

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Summary of the invention

The invention provides a kind of distributed heterogeneous network resource allocation method based on CoMP transmission, including walking as follows Rapid:

A. receiving step, receives the parameter of input；

B. generating probability step, initializes allocative decision generating probability；

C. generate sample step, generate allocative decision sample according to generating probability；

D. calculation procedure, calculates the target function value of each sample；

E. screen step, sample is screened, and export a scheduling result；

F. judge step, it is judged that whether algorithm restrains, the most so perform scheduling result output step, otherwise according to complete The intermediate object program of base station, portion updates generating probability and then parameter performs to generate sample step；

G. scheduling result output step, exports RB scheduling result.

As a further improvement on the present invention, in described generating probability step, TP m is the probability of RB scheduling on RB n Distribution initial value is expressed as

TP m probability distribution q that user selects on RB n is can get according to above formula^m,n, and then TP m can be obtained at whole RB On user's select probability distribution

As a further improvement on the present invention, in described screening step, it is known that the capacity of the back haul link of TP m is C_m, During generating sample, if the handling capacity of the TP m of sample generationSampleTo directly be removed；With Sample ground, orderRepresent the thresholding of sample utility function in the t time iteration, the sample of requirement can not be reached for utility function(i.e.) also will be removed；N is generated according to above-mentioned requirements_SAMIndividual effective sample, is designated as

As a further improvement on the present invention, in described screening step, to N_SAMThe utility function of individual effective sample is carried out Descending, it is assumed thatSet a quantile ρ (0≤ρ≤1), for descending The sample of arrangement, intercepts whereinIndividual sample is as significant samples and general using these samples as updating The foundation of rate, and the utility function thresholding generating effective sample will be stepped upEvery time after iteration, this thresholding will be updated to The minimum of a value of utility function in significant samples, i.e.

$f_{thres}^{(t+1)}=f_m\left(X_{N_{IM}}^m\right)---\left(17\right).$

Present invention also offers a kind of distributed heterogeneous network resource distribution system based on CoMP transmission, including:

Receiver module, for receiving the parameter of input；

Generating probability module, is used for initializing allocative decision generating probability；

Generate sample module, for generating allocative decision sample according to generating probability；

Computing module, for calculating the target function value of each sample；

Screening module, for screening sample, and exports a scheduling result；

Whether judge module, restrain for evaluation algorithm, the most so performs scheduling result output module, otherwise basis All the intermediate object program of base station updates generating probability and then parameter performs to generate sample module；

Scheduling result output module, is used for exporting RB scheduling result.

As a further improvement on the present invention, in described generating probability module, TP m is the probability of RB scheduling on RB n Distribution initial value is expressed as

TP m probability distribution q that user selects on RB n is can get according to above formula^m,n, and then TP m can be obtained at whole RB On user's select probability distribution

As a further improvement on the present invention, in described screening module, it is known that the capacity of the back haul link of TP m is C_m, During generating sample, if the handling capacity of the TP m of sample generationSampleTo directly be removed；With Sample ground, orderRepresent the thresholding of sample utility function in the t time iteration, the sample of requirement can not be reached for utility function (i.e.) also will be removed；N is generated according to above-mentioned requirements_SAMIndividual effective sample, is designated as

As a further improvement on the present invention, in described screening module, to N_SAMThe utility function of individual effective sample is carried out Descending, it is assumed thatSet a quantile ρ (0≤ρ≤1), for descending The sample of arrangement, intercepts whereinIndividual sample is as significant samples and general using these samples as updating The foundation of rate, and the utility function thresholding generating effective sample will be stepped upEvery time after iteration, this thresholding will be updated to The minimum of a value of utility function in significant samples, i.e.

$f_{thres}^{(t+1)}=f_m\left(X_{N_{IM}}^m\right)---\left(18\right).$

The invention has the beneficial effects as follows: first the present invention sets sample generating probability according to the user data rate estimated, Effectively accelerate convergence of algorithm speed.Then, by the continuous iteration to generating probability, it is met back haul link capacity The optimal scheduling result limited.Finally, scheduling result is modified by algorithm according to given data rate threshold, has ensured biography Defeated minimum data rate requirement.By emulation experiment, the resource allocation algorithm demonstrating present invention proposition gulps down in raising system The validity of the aspects such as the amount of telling, energy efficiency and user fairness and feasibility.

Accompanying drawing explanation

Fig. 1 is the system model figure of the present invention；

Fig. 2 is that the topological schematic diagram of the emulation of the present invention is (with the relative position of coordinate position simulation TP with user, 1 unit =1m)；

Fig. 3 is the total data rate figure of every TP under different back haul link capacity limit；

Fig. 4 is the fairness coefficient figure of system under different backhaul efficiency capacity limit；

Fig. 5 is the energy efficiency figure of system under different back haul link capacity limit；

Fig. 6 is the total data rate figure of every TP under different QoS requires；

Fig. 7 is the energy efficiency figure that different QoS requires lower system；

Fig. 8 is the method flow diagram of the present invention.

Detailed description of the invention

As shown in Figure 8, the invention discloses a kind of distributed heterogeneous network resource allocation method based on CoMP transmission, bag Include following steps:

Step S1, receives the parameter of input；

Step S2, initializes allocative decision generating probability；

Step S3, generates allocative decision sample according to generating probability；

Step S4, calculates the target function value of each sample；

Step S5, screens sample, and exports a scheduling result；

Step S6, it is judged that whether algorithm restrains, the most so performs step S7, otherwise according to the middle junction of whole base stations Fruit updates generating probability and then parameter performs step S3；

Step S7, exports RB scheduling result.

In step sl, parameter includes UE quantity, the accumulation transmission capacity of each UE, RB quantity, constant power apportioning cost, wink Time CSI, CoMP set select.

Illustrate for:

1 method has independently been calculated according to global channel information by each TP.

2 methods first according to input information and formula (12) produce initial sample generating probability.

3 generate the sample of a large amount of RB scheduling according to sample generating probability, and carry out the screening of sample according to given code.

If 4 samples meet the condition of convergence, then export the RB scheduling result on this TP.

If 5 samples are unsatisfactory for the condition of convergence, then according to the Sample Refreshment generating probability after screening and relevant parameter, lay equal stress on Newly-generated sample.

6 successive ignition are until the sample generated meets the condition of convergence, and export accordingly result.

The system model of present invention research is a heterogeneous network downlink transmission system using FDD, network structure As shown in Figure 1.Network has eNB or the low-power transmission node (transmission point, TP) of M isomery, often It is furnished with N on individual TP_TIndividual transmitting antenna.The set expression of all transmission nodes be Π=1 ..., M}.In network, each TP is common Multiplexing N_RBThe a width of 180kHz of individual band, Transmission Time Interval are the Resource Block of 1ms.Network at a time, simultaneously for being uniformly distributed K user provide transmission service.The set expression of all users be Λ=1 ..., K}, each subscriber equipment is furnished with N_T Individual reception antenna.

Feel at a centralized control unit (CU) in logic in a network, be responsible for collecting the channel letter that user detects Breath, and on this basis the resource such as system frequency, power is allocated according to certain rule, to obtain optimal systematicness Energy.CU connected by back haul link send control signaling to each TP, resource allocation result and data message waiting for transmission.By In the existence of back haul link, can with between reasonable assumption TP, between TP and centralized control unit in each dimension (time, frequency etc.) Upper Complete Synchronization.This is also one of necessary condition using CoMP technology.User uses in can distributing community according to its channel status Family and Cell Edge User.For an intra-cell users, due to this user may on geographical position close in network certain TP, therefore user receive from reference signal (reference signal, the RS) intensity closing on TP, hence it is evident that be better than from The reference signal of other TP.On the contrary, Cell Edge User is owing to being in the overlapping region of multiple TP coverage, it receives The reference signal strength from several TP closed on more weak and gap is the least.Two kinds of user can select with reference to strong The TP of degree maximum is as home TP, and essential information is registered in home TP list.Additionally, present invention assumes that between TP Complete Synchronization in time, frequency.Wireless channel has pseudo-fading characteristic smoothly, i.e. at a Transmission Time Interval (transmission time interval, TTI) interior characteristic of channel does not changes.

The CoMP set of the present invention selects:

With reference to the definition of LTE-A, the reference signal situation that system allows user to receive according to self selects a CoMP Set, this set comprises the TP that may transmit data for user.It should be noted that CoMP set may comprise one or Multiple TP.The CoMP set of user k is designated asThe principle that in LTE-A, Π k selects is:

Wherein, Δ_thresRepresenting that CoMP collection selects threshold value (dB), RS represents the receiving intensity of reference signal.

Work as Δ_thresMore hour, the TP comprised in CoMP set is the most, the conjunction that transmission obtains when using cooperation transmission technology Make gain the biggest.Correspondingly, required during CoMP transmission system control overhead is consequently increased.Therefore, Δ_thresSelection anti- Reflect the tradeoff of cooperation gain and overhead, can be suitably to Δ for network environment and system requirements_thresAdjust Whole.According to the suggestion in bibliography [12], make Δ_thres=5dB.

According to the criterion in formula (1), user k selects set Π_kAnd selection result is fed back to centralized control unit.If Π_kIn only comprise the home TP of user k, i.e. | Π_k|=1, then k is intra-cell users, and in downlink transfer, only home TP is Its service.On the contrary, if | Π_k| ＞ 1, then explanation k is Cell Edge User.Several TP in CoMP set are with given CoMP approach to cooperation combine for user k service, to improve the service quality of user.

Joint transmission CoMP of the present invention:

Joint transmission CoMP is one of typical realisation of JP CoMP transmission.In JT CoMP, the CoMP collection of user k Close Π_kIn whole or multiple TP on the same RBs for user k send identical data.Due to being spatially separating between TP, data After different TP send, the channel through space independence arrives user's reception antenna, by merging make the intensity of data-signal with Send TP to increase and improve, i.e. obtain space diversity gain.It is believed that JT CoMP technology is by dry between the non-primary cell of user Disturb and become available transmission, both improve data signal strength, and decreased presence of intercell interference simultaneously.

The mode that is directly realized by of JT CoMP technology is, each transmission of user k all utilizes CoMP set Π_kIn whole TP is as transmission TP, and this fixing strategy is referred to as FJT (fixed JT).FJT CoMP does not accounts for the frequency of channel and selects characteristic, The dynamic change of network cannot be adapted to.In order to solve this problem, system can also be dynamic according to the transient channel information of user Ground selects CoMP cooperation set for user on each RB, to reach the lifting of network in general performance, and this JT the most flexibly CoMP technology is also referred to as DYNAMIC J T (DJT) CoMP.Order setRepresent user k CoMP cooperation set on RBn, then at DJT In CoMP transmission, user k transmission on RB n is represented by:

$y_k^n=\underset{m\∈{\mathrm{\Ω}}_k^n}{\mathrm{\Σ}}{H}_{m,k}^n{w}_m^n\sqrt{p_m^n}{s}_m^n+\underset{m^{\′}\∈\mathrm{\Π}\backslash {\mathrm{\Ω}}_k^n}{\mathrm{\Σ}}{H}_{m^{\′},k}^n{w}_{m^{\′}}^n\sqrt{p_{m^{\′}}^n}{s}_{m^{\′}}^n+n_k^n---\left(2\right)$

Wherein, { m} represents from the Π removal element { set after m} Π；It is N_R× 1 dimension receives vector, the most each unit Element represents the reception signal of antenna on correspondence position；For N_R×N_TThe channel matrix of dimension, wherein elementRepresent TP Channel coefficients between jth antenna and the i-th antenna of user k of m；It is N_T× 1 dimensional vector, represents that TPm is to symbol Precoding, and have It is the TP m transmitting power to this transmission distribution；It it is the multiple height at reception antenna This white noise vector,

Definition scheduling index setWherein System centre control unit is described The RB n distributing TP m in ensuing TTI is that user k transmits data, i.e.Correspondingly, formula (2) receives signal Signal to Interference plus Noise Ratio is represented by:

${\mathrm{\γ}}_k^n=\frac{\underset{m\∈{\mathrm{\Ω}}_k^n}{\Σ}\left|\right|H_{m,k}^n{w}_m^n|{|}^2{p}_m^n}{\underset{m^{\′}\∈\mathrm{\Π}\backslash {\mathrm{\Ω}}_k^n}{\Σ}\left|\right|H_{m^{\′},k}^n{w}_{m^{\′}}^n|{|}^2{p}_{m^{\′}}^n+{\mathrm{\σ}}^2}=\frac{\underset{m\∈\mathrm{\Π}}{\Σ}{\mathrm{\β}}_{m,k}^n\left|\right|H_{m,k}^n{w}_m^n|{|}^2{p}_m^n}{\underset{m^{\′}\∈\mathrm{\Π}}{\Σ}(1-{\mathrm{\β}}_{m^{\′},k}^n)\left|\right|H_{m^{\′},k}^n{w}_{m^{\′}}^n|{|}^2{p}_{m^{\′}}^n+{\mathrm{\σ}}^2}---\left(3\right)$

Wherein,

Optimization problem models:

In order to take into account the fairness between the handling capacity of system, energy ezpenditure and user simultaneously, the optimization that resource is distributed Object definition is following form:

$\max \underset{k=1}{\overset{K}{\Σ}}\underset{n=1}{\overset{N_{RB}}{\Σ}}\frac{R_k^n}{{\stackrel{\‾}{R}}_k}/\underset{m=1}{\overset{M}{\Σ}}\underset{n=1}{\overset{N_{RB}}{\Σ}}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\β}}_{m,k}^n{p}_m^n---\left(4\right)$

Wherein,Representing user's data rate on RB n, its calculating formula is:

$R_k^n=b\lg (1+{\mathrm{\γ}}_k^n)---\left(5\right)$

The cumulative mean data speed obtained in current TTI position for user k, it is defined as^[13]:

${\stackrel{\‾}{R}}_k=\mathrm{\α}{\stackrel{\‾}{R}}_k^{before}+(1-\mathrm{\α})R_k---\left(6\right)$

Wherein, 0 < α < 1 is forgetting factor, is used for balancing cumulative mean and distributes resource according to speed and current data rate Impact；For by current time, the cumulative mean of user k is according to speed.

But such object function can cause extreme case to occur: in order to improve energy efficiency, system may drop excessively Low transmitting power, thus seriously destroy the quality of transmission.In order to avoid the appearance of this possibility, the present invention is in constraint Transmission quality is limited by condition.To sum up, the optimization problem mould of heterogeneous network resource allocation problem based on CoMP technology Type is represented by:

$\begin{array}{cc}\underset{{\mathrm{\&beta;}}_{m,k}^n,p_m^n}{\max }& \underset{k=1}{\overset{K}{\&Sigma;}}\underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\frac{R_k^n}{{\stackrel{\&OverBar;}{R}}_k}/\underset{m=1}{\overset{M}{\&Sigma;}}\underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\underset{k=1}{\overset{K}{\&Sigma;}}{\mathrm{\&beta;}}_{m,k}^n{p}_m^n\end{array}$

$\begin{array}{ccc}s.t.& C1:& 0\&le;p_m^n\&le;S,\&ForAll;m,n\end{array}$

$\begin{array}{cc}C2:& {\mathrm{\&beta;}}_{m,k}^n\&Element;\{0,1\},\underset{k=1}{\overset{K}{\mathrm{\&Sigma;}}}{\mathrm{\&beta;}}_{m,k}^n\&le;1,\&ForAll;m,n,k\end{array}---\left(7\right)$

$\begin{array}{cc}C3:& \underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\underset{k=1}{\overset{K}{\&Sigma;}}{\mathrm{\&beta;}}_{m,k}^n{R}_k^n\&le;C_m,\&ForAll;m\end{array}$

$\begin{array}{cc}C4:& R_k^n\&GreaterEqual;R_{thres},\&ForAll;k,n\end{array}$

Wherein, C1 represents that the be limited to S, C2 that launch power of the highest TP represents that RB can not be duplicatedly distributed, and C3 represents back Every TP is handled up quantitative limitation by journey capacity of trunk；R_thresFor given data rate threshold, the Resource Allocation Formula of system should be protected The data rate demonstrate,proving each transmission is not less than this threshold value, the quality of each transmission during therefore C4 ensure that network.

Distributed resource allocation algorithm based on cross-entropy method:

Cross entropy (cross entropy, CE) algorithm initially by Rubinstein 1997 propose, for complexity with In machine network, the probability of rare event is estimated^[14].Subsequently, Rubinstein finds simply to repair Cross-Entropy Algorithm Just, just can be used to combinatorial optimization problem is solved^[15]。

For the ease of analyzing, it is assumed that the power on each RB is assigned as equivalence distribution, then RB scheduling problem can be reduced to:

$\begin{array}{cc}\underset{{\mathrm{\&beta;}}_{m,k}^n}{\max }& \underset{k=1}{\overset{K}{\&Sigma;}}\underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\frac{R_k^n}{{\stackrel{\&OverBar;}{R}}_k}/\underset{m=1}{\overset{M}{\&Sigma;}}\underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\underset{k=1}{\overset{K}{\&Sigma;}}{\mathrm{\&beta;}}_{m,k}^n{p}_m^n\end{array}$

$\begin{array}{ccc}s.t.& C1:& p_m^n=S,\&ForAll;m,n\end{array}$

$\begin{array}{cc}C2:& {\mathrm{\&beta;}}_{m,k}^n\&Element;\{0,1\},{\mathrm{\&Sigma;}}_{k=1}^K{\mathrm{\&beta;}}_{m,k}^n\&le;1,\&ForAll;m,n,k\end{array}---\left(8\right)$

$\begin{array}{cc}C3:& \underset{n=1}{\overset{N_{RB}}{\&Sigma;}}\underset{k=1}{\overset{K}{\&Sigma;}}{\mathrm{\&beta;}}_{m,k}^n{R}_k^n\&le;C_m,\&ForAll;m\end{array}$

$\begin{array}{cc}C4:& R_k^n\&GreaterEqual;R_{thres},\&ForAll;k,n\end{array}$

Variable in formula (8)Be a dimension be M × N_RBThe random matrix of × K, whereinIt it is a bit Number, can be considered Bernoulli random variable.In each iteration of cross-entropy method, need to produce the sample of q.s.If directly RightSolve, it is clear that amount of calculation is the biggest.

In order to reduce computation complexity, it is considered to generate sample respectively for each TP.A sample on note TP m is X^m =[x^m(1),...,x^m(n),...,x^m(N_RB)], wherein x^mN () represents the user that TP m will service on the n-th RB, i.e.The user making each TP gathers Λ_m.Sample X^mIn element x^mN () is then according to giving probability distribution from set Λ_mChoosing Take.Such sample design, can effectively reduce sample space and computation complexity.

The present invention utilizes above-mentioned sample design, it is proposed that a kind of heuristic mutation operations strategy based on cross-entropy method.Should Strategy can be roughly divided into 3 parts: probability initializes, sample iteration and modified result.Wherein, sample iterative part is algorithm Core, generates including sample, screens and probability distribution renewal.Iterative process finally makes convergence of probability distribution determine in one As a result, i.e. the optimal solution of scheduling problem.Algorithm uses distributed processing mode to dispatch separated from one another by the RB of every TP, therefore calculates Method can be independently executed by each TP.Although such algorithm have lost certain computational accuracy, but it is required to shorten calculating Time.

Probability distribution initializes:

In system, each TP m, according to current channel condition and given strategy, gathers from itChoose user Suitable RB is transmitted.Wherein, 0 represents that TP m does not implement transmission on this RB.Note TP m TP m scheduling on RB n The probability distribution of strategy is vectorRepresent.Wherein,Expression system exists Select to user on this RBTransmission is (i.e.) probability, and meetAccording toDefinition UnderstandAnd corresponding probabilityThen represent that TP m does not occur the probability of any transmission on RB n.Initializing Cheng Zhong, is set as constant by this probability, it may be assumed that

$q_1^{m,n}=\mathrm{Pr}\_0---\left(9\right)$

ForIn nonzero element, then according to the estimation of user data rate, probability is composed initial value.Such assignment Convergence rate can be accelerated to a certain extent.Channel situation according to user and the selection of cooperation set, use JT CoMP strategy Time, user k data rate on RB n estimated by available formula (10):

${\tilde{R}}_k^n=b\lg \left(1+\frac{\underset{m\&Element;{\mathrm{\&Pi;}}_k}{\&Sigma;}\left|\right|H_{m,k}^n{w}_m^n|{|}^{2}S}{\underset{m^{\&prime;}\&Element;\mathrm{\&Pi;}\backslash {\mathrm{\&Pi;}}_k}{\&Sigma;}\left|\right|H_{m^{\&prime;},k}^n{w}_{m^{\&prime;}}^n|{|}^{2}S+{\mathrm{\&sigma;}}^2}\right)---\left(10\right)$

The QoS that every RB is transmitted by constraints C4 in formula (8) is limited, it is impossible to reach threshold value R_thresIt is considered as Unsuccessful transmission.In order to economize on resources, system will not consider to dispatch on RB nUser k, even k is corresponding ProbabilityAnd for data rate threshold R can be reached_thresUser, then the data rate estimated according to it The proportion accounting for total data rate carries out probability assignment.Assume that the data rate of user k meets qos requirement, the probability of its correspondenceIt is defined as:

Wherein, on the right of equal sign, the data rate of the 1st expression user accounts for the proportion of total data rate, it should be noted that Total data rate only comprises the data rate that disclosure satisfy that QoS；The 2nd on the right of equal sign is then to ensure that probability distribution q^m,nMeet

In sum, TP m probability distribution initial value of RB scheduling on RB n can be expressed as:

According to formula (12) available TP m probability distribution q that user selects on RB n^m,n, and then TP m can be obtained all User's select probability distribution on RBAt the iteration initial stage, algorithm is according to q^mGenerate sample, then root According to the sample situation update probability distribution after screening, until convergence of probability distribution.

Sample iteration:

In the iterative process of algorithm, system is according to given several samples of probability distribution stochastic generation.Stochastic generation Sample do not ensure that the optimal solution being to meet constraints, it is therefore desirable to sample is screened.Sample quilt after screening It is considered the sample of " good ", and according to the Sample Refreshment probability distribution of " good ", it is possible to make algorithm when next iteration with higher Probability obtain " more preferably " sample.Concrete sample iterative process is as described below.

According to known probability distribution for TP m stochastic generation sample i, it is designated as According toThe corresponding scheduling about TP m can be obtained index

$\left\{\begin{array}{c}{\mathrm{\&beta;}}_{m,k}^n=1,k=x_i^m\left(n\right)\\ {\mathrm{\&beta;}}_{m,k}^n=0,k\&NotEqual;x_i^m\left(n\right)\end{array}\right.---\left(13\right)$

According to the scheduling result obtained, user k transmits the data rate of acquisition on RB n and becomes:

$R_k^n=b\lg \left(1+\frac{\underset{m\&Element;\mathrm{\&Pi;}}{\&Sigma;}{\mathrm{\&beta;}}_{m,k}^n\left|\right|H_{m,k}^n{w}_m^n|{|}^2{p}_m^n}{\underset{m^{\&prime;}\&Element;\mathrm{\&Pi;}}{\&Sigma;}(1-{\mathrm{\&beta;}}_{m^{\&prime;},k}^n)\left|\right|H_{m^{\&prime;},k}^n{w}_{m^{\&prime;}}^n|{|}^2{p}_{m^{\&prime;}}^n+{\mathrm{\&sigma;}}^2}\right)---\left(14\right)$

Make f_mRepresent utility function corresponding to TP m, according to formula (8) it is known that at sampleThe effectiveness that lower TP m obtains Functional value is:

$f_m\left(X_i^m\right)=\underset{n=1,x_i^m\left(n\right)\&NotEqual;0}{\overset{N_{RB}}{\&Sigma;}}\frac{R_{x_i^m\left(n\right)}^n}{{\stackrel{\&OverBar;}{R}}_{x_i^m\left(n\right)}}/\underset{n=1,x_i^m\left(n\right)\&NotEqual;0}{\overset{N_{RB}}{\&Sigma;}}S---\left(15\right)$

Wherein,S represents according to sampleThe total power consumption of the TP m obtained.

SampleThe corresponding total throughout on TP m is represented by:

$R_m\left(X_i^m\right)=\underset{n=1}{\overset{N_{RB}}{\&Sigma;}}{R}_{x_i^m\left(n\right)}^n---\left(16\right)$

Qualified sample should meet two conditions: first, and the utility function value of sample is sufficiently high；Second, sample need to be expired Constraints C3 in foot back haul link capacity limit, i.e. formula (8).The sample randomly generated does not ensures that and meets the two bar Part, it is therefore desirable to the sample generated is screened.

The capacity of known TP m back haul link is C_m, during generating sample, if the TP m's of sample generation handles up AmountSampleTo directly be removed.Similarly, orderRepresent the threshold of sample utility function in the t time iteration Value, can not reach the sample of requirement for utility function(i.e.) system will not consider yet.System is according to upper State requirement and generate N_SAMIndividual effective sample, is designated as

Further, algorithm according to the principle of important sampling at N_SAMIndividual effective sample filters out significant samples.To N_SAM The utility function of individual effective sample carries out descending, without loss of generality, it can be assumed that Set a quantile ρ (0≤ρ≤1), for the sample of descending, intercept whereinIndividual sample is made For significant samples, and using these samples as the foundation of update probability.SymbolRepresent and a is rounded up.In order to the most repeatedly Making sample results closer to optimum object function in Dai, algorithm generates the utility function threshold value of effective sample by stepping upEvery time after iteration, this threshold value will be updated to the minimum of a value in significant samples in utility function value, i.e.

$f_{thres}^{(t+1)}=f_m\left(X_{N_{IM}}^m\right)---\left(17\right)$

According to such more new regulation, the utility function of sample will become closer to optimal solution.

It follows that algorithm is distributed according to significant samples update probability so that can be with more preferable probability in next iteration Generate the sample of " good ".SampleMiddle elementProbability distribution q^m,n, can be according to N_IMIn each user (include No user situation) number of times that occurs is updated, it may be assumed that

Wherein,Represent at N_IMIn individual sample, u occurs in the number of times of n-th of sample.At next In secondary iteration, algorithm can generate new N according to the probability distribution after updating_SAMIndividual sample.After iteration several times, probability divides Cloth q^m,nProgressively restrain.As whole q^m,nAll convergence with probability 1 is when a certain user, and algorithm i.e. obtains the optimal solution of RB scheduling, and The optimal solution of sample (sample generated with probability 1) the i.e. problem of the determination now obtained.

Algorithm 1 summarizes the process in every single-step iteration.

In algorithm 1, t represents current iterations,Scheduling knot for the TP m of the output of the algorithm when convergence in probability Really.In each element representation TP m correspondence position RB on scheduling user, represent i.e. with scheduling index Therefore, byScheduling index set can be obtained

Modified result:

Section 3.1, the result that the Cross-Entropy Algorithm described obtains, strictly meets restrictive condition C1～C3, but can not protect Card all transmission all meet given threshold value (i.e. restrictive condition C4).The final goal of the system resource optimization distribution discussed is to carry Energy-efficient, for the purpose of the saving energy, the transmission that algorithm will select closedown can not meet qos requirement.

Algorithm 2 summarizes based on cross entropy the distributed RB dispatching algorithm that the present invention proposes.

Noting, algorithm 2 is the computing for a certain TP m, has to the scheduling result on TP mIn other wordsSystem needs to carry out each TP the distributive operation described in algorithm 2 to obtain whole scheduling resultStill can not get the accurate estimation to data rate after having performed the computing in algorithm 2 due to every TP, i.e. without Method is knownThus the modified result in can not performing such as algorithm 1 in distributed dispatching algorithm.This causes distributed algorithm In the transmission that the scheduling result obtained produces, it is understood that there may be qos requirement can not be met.Emulation experiment thinks such transmission Can not be correctly received, it may be said that underproof transmission had both consumed energy, the most not obtain corresponding data rate, thus Result in the reduction of energy efficiency in distributed formula algorithm.

The invention also discloses a kind of distributed heterogeneous network resource distribution system based on CoMP transmission, including:

Receiver module, for receiving the parameter of input；

Generating probability module, is used for initializing allocative decision generating probability；

Generate sample module, for generating allocative decision sample according to generating probability；

Computing module, for calculating the target function value of each sample；

Screening module, for screening sample, and exports a scheduling result；

Whether judge module, restrain for evaluation algorithm, the most so performs scheduling result output module, otherwise basis All the intermediate object program of base station updates generating probability and then parameter performs to generate sample module；

Scheduling result output module, is used for exporting RB scheduling result.

Simulation result and discussion:

Emulation utilizes SCM (space channel model)^[16]Model generation is believed without the MIMO of line-of-sight transmission urban area circumstance Road, the major parameter of employing is shown in Table 1.Fig. 2 shows the dense network environments in emulation.Network comprises 37 covering radius altogether For the hexagonal cell of 250m, the most outermost 18 TP do not produce the transmission of reality, but the transmission of internal layer TP is with mould Intend the presence of intercell interference in real scene.This analog form is referred to as community coiling, is conventional in large scale network emulation Means^[17]。

Table 1 simulation parameter

The topology signal (with the relative position of coordinate position simulation TP with user, 1 unit=1m) of Fig. 2 emulation

First emulation experiment discusses the impact on systematic function of the back haul link capacity limit.Experiment considers without flyback line Road capacity limit, 100Mbit/s back haul link capacity and 3 kinds of situations of 50Mbit/s back haul link capacity.Fig. 3 gives different returning Journey capacity of trunk limits the total data rate of every TP in lower network.It can be seen that in unrestricted and the 100Mbit/s flyback line appearance of a street In the case of amount, in system, the total data rate of every TP can reach about 30Mbit/s.And when back haul link capacity limit exists During 50Mbit/s, the total data rate of every TP drops to 15～18Mbit/s.The result shows back haul link capacity limit System data rates is had a strong impact on.

In order in the system fairness of user data rate is clearly viewed, it is fair to present invention employs in bibliography [18] The definition of property coefficient F:

$F=\frac{{\left({\mathrm{\&Sigma;}}_{k\&Element;\mathrm{\&Lambda;}}{R}_k\right)}^2}{{\mathrm{K\&Sigma;}}_{k\&Element;\mathrm{\&Lambda;}}{R}_k^2}---\left(19\right)$

Fig. 4 shows the user data rate fairness of system under different back haul link capacity limit, and result shows 50Mbit/ Under s back haul link capacity, being decreased obviously occurs in the fairness of system.Under unrestricted or 100Mbit/s back haul link capacity, it is The fairness coefficient of system may be up to 0.75.But, when back haul link capacity drops to 50Mbit/s, the fairness that system obtains Coefficient is the highest by only 0.48.This is because, when back haul link capacity constraint, in order to reduce the load of back haul link, system is not Obtain the quantity not reducing CoMP transmission.Therefore, the Cell Edge User of Service Promotion originally can be obtained by CoMP transmission, Enough data rates cannot be obtained, thus result in the decline of system entirety fairness.

Fig. 5 gives the energy efficiency of system under different back haul link capacity limit.The result being different from Fig. 3 and Fig. 4, Result in Fig. 5 shows that back haul link capacity limit can't cause the reduction of system energy efficiency.This is because, due to backhaul The limited transmission quantity causing system to be implemented in every TTI of capacity of trunk declines, and the most correspondingly energy ezpenditure also decreases, Therefore system energy efficiency is ensured.

Emulation experiment has also been inquired into different QoS and has been required the impact on systematic function.Fig. 6 gives under different QoS requirement, net The mean data rate of every TP in network.It can be seen that along with the increase of QoS, the data rate of system has obtained corresponding lifting. Work as R_thresDuring for 180kbit/s, the mean data rate of the every TP of system is only slightly above 26Mbit/s；Promote R_thresArrive During 360kbit/s, the mean data rate of every TP rises to more than 28Mbit/s；And work as R_thresDuring for 540kbit/s, every TP's Mean data rate can be close to 30Mbit/s.

Fig. 7 gives the energy efficiency that different QoS requires lower system.It can be seen that R_thresFor 180kbit/s and The energy efficiency that during 360kbit/s, system obtains is close.But work as R_thresWhen bringing up to 540kbit/s, the energy efficiency of system Slightly promote.This is because, work as R_thresBring up to the transmission quantity that during 540kbit/s, system is implemented in every TTI reduce, accordingly Energy ezpenditure decrease.Meanwhile, the transmission every time implemented due to system security can obtain higher data speed Rate, therefore the total data rate of network is higher.The high data rate of system and low-energy-consumption have ultimately resulted in system capacity effect The lifting of rate.

Heterogeneous network based on CoMP can effectively promote spectrum efficiency and the energy efficiency of GSM, and reasonable Efficient radio resource allocation strategy is the important prerequisite ensureing dense network systematic function.The present invention is according to based on CoMP different Structure network characteristics, it is proposed that a kind of distributed resource allocation algorithm based on cross-entropy method.Algorithm is first according to the use estimated User data speed sets sample generating probability, effectively accelerates convergence of algorithm speed.Then, continuous by generating probability Iteration, is met the optimal scheduling result of back haul link capacity limit.Finally, algorithm is according to given data rate threshold Scheduling result is modified, has ensured the minimum data rate requirement of transmission.By emulation experiment, demonstrate the present invention and propose Resource allocation algorithm improving validity and the feasibility of the aspects such as throughput of system, energy efficiency and user fairness.

Above content is to combine concrete preferred embodiment further description made for the present invention, it is impossible to assert Being embodied as of the present invention is confined to these explanations.For general technical staff of the technical field of the invention, On the premise of present inventive concept, it is also possible to make some simple deduction or replace, all should be considered as belonging to the present invention's Protection domain.

Claims (8)

1. a distributed heterogeneous network resource allocation method based on CoMP transmission, it is characterised in that comprise the steps:

A. receiving step, receives the parameter of input；

B. generating probability step, initializes allocative decision generating probability；

C. generate sample step, generate allocative decision sample according to generating probability；

D. calculation procedure, calculates the target function value of each sample；

E. screen step, sample is screened, and export a scheduling result；

F. judge step, it is judged that whether algorithm restrains, the most so perform scheduling result output step, otherwise according to whole bases The intermediate object program stood updates generating probability and then parameter performs to generate sample step；

G. scheduling result output step, exports RB scheduling result.

Distributed heterogeneous network resource allocation method the most according to claim 1, it is characterised in that in described generating probability In step, TP m probability distribution initial value of RB scheduling on RB n is expressed as

TP m probability distribution q that user selects on RB n is can get according to above formula^m,n, and then TP m can be obtained on whole RB User's select probability distribution q^m={ q^m,n}

Distributed heterogeneous network resource allocation method the most according to claim 1, it is characterised in that in described screening step In, it is known that the capacity of the back haul link of TP m is C_m, during generating sample, if the handling capacity of the TP m of sample generationSampleTo directly be removed；Similarly, orderRepresent the door of sample utility function in the t time iteration Limit, can not reach the sample of requirement for utility function(i.e.) also will be removed；Raw according to above-mentioned requirements Become N_SAMIndividual effective sample, is designated as

Distributed heterogeneous network resource allocation method the most according to claim 3, it is characterised in that in described screening step In, to N_SAMThe utility function of individual effective sample carries out descending, it is assumed that Set a quantile ρ (0≤ρ≤1), for the sample of descending, intercept whereinIndividual sample is made For significant samples, and using these samples as the foundation of update probability, and the utility function generating effective sample will be stepped up ThresholdingEvery time after iteration, this thresholding will be updated to the minimum of a value of utility function in significant samples, i.e.

$f_{thres}^{(t+1)}=f_m\left(X_{N_{IM}}^m\right)---\left(17\right).$

5. a distributed heterogeneous network resource distribution system based on CoMP transmission, it is characterised in that

Including:

Receiver module, for receiving the parameter of input；

Generating probability module, is used for initializing allocative decision generating probability；

Generate sample module, for generating allocative decision sample according to generating probability；

Computing module, for calculating the target function value of each sample；

Screening module, for screening sample, and exports a scheduling result；

Whether judge module, restrain for evaluation algorithm, the most so performs scheduling result output module,

Otherwise update generating probability according to the intermediate object program of whole base stations and then parameter performs to generate sample module；

Scheduling result output module, is used for exporting RB scheduling result.

Distributed heterogeneous network resource distribution system the most according to claim 5, it is characterised in that in described generating probability In module, TP m probability distribution initial value of RB scheduling on RB n is expressed as

TP m probability distribution q that user selects on RB n is can get according to above formula^m,n, and then TP m can be obtained on whole RB User's select probability distribution q^m={ q^m,n}

Distributed heterogeneous network resource distribution system the most according to claim 5, it is characterised in that in described screening module In, it is known that the capacity of the back haul link of TP m is C_m, during generating sample, if the handling capacity of the TP m of sample generationSampleTo directly be removed；Similarly, orderRepresent the door of sample utility function in the t time iteration Limit, can not reach the sample of requirement for utility function(i.e.) also will be removed；Raw according to above-mentioned requirements Become N_SAMIndividual effective sample, is designated as

Distributed heterogeneous network resource distribution system the most according to claim 7, it is characterised in that in described screening module In, to N_SAMThe utility function of individual effective sample carries out descending, it is assumed that Set a quantile ρ (0≤ρ≤1), for the sample of descending, intercept whereinIndividual sample is made For significant samples, and using these samples as the foundation of update probability, and the utility function generating effective sample will be stepped up ThresholdingEvery time after iteration, this thresholding will be updated to the minimum of a value of utility function in significant samples, i.e.

$f_{thres}^{(t+1)}=f_m\left(X_{N_{IM}}^m\right)---\left(17\right).$