CN103428770B - Flow allocation method in the multi-link parallel transmission of heterogeneous wireless network - Google Patents

Abstract

Flow allocation method in the multi-link parallel transmission of a kind of heterogeneous wireless network: according to the transmission performance parameter that in heterogeneous wireless network, each connects, data estimator bag is by the different propagation delay time connected, introduce Global Genetic Simulated Annealing Algorithm calculation optimization assignment of traffic ratio again, consider multiple performance parameters of the maximum transmission rate of each Internet resources, mean transit delay, the error rate and maximum retransmission simultaneously, there is provided a kind of feasible flow allocation method, to realize the assignment of traffic minimizing maximum transmitted delay inequality between each connection.The present invention utilizes the performance of climbing the mountain of the Implicit Parallelism of genetic algorithm and annealing algorithm, effectively reduces the speed that computation complexity, greatly quickening solve and restrain, arranges the further equalization algorithm performance of convergence threshold value and elapsed time.Compared with other flow allocation methods, the present invention is more suitable for the embody rule scene of heterogeneous wireless network, and take full advantage of network connection resource, throughput is larger.

Description

Flow allocation method in the multi-link parallel transmission of heterogeneous wireless network

Technical field

The present invention relates to a kind of assignment of traffic technology of the parallel transmission for heterogeneous wireless network, specifically, relate to the flow rate ratio distribution method of multiple connection parallel transmission in a kind of heterogeneous wireless network, belong to the mobile communication technology field in radio communication.

Background technology

Following cordless communication network system will be one kind of multiple heterogeneous networks (such as comprising: the standards such as 2G/2.5G/3G, LTE, WLAN, WPAN) and deposit, collaborative and complex system that is that constantly merge.The characteristic of these heterogeneous networks in overlay area, bandwidth, reliability, cost and fail safe etc. is different.And they will to meet the QoS demand of terminal use jointly in the mode complemented each other in following one period of long term.

The development of multi-mode terminals, made to use simultaneously multiple heterogeneous network transmitting data in parallel become may with reality.Multi-link parallel transmission technology under heterogeneous network can make full use of Internet resources, meets future communications business demand.But because the various connected modes of heterogeneous wireless network all there are differences in technical characterstic and load capacity, therefore, multi-link parallel transmission certainly will bring problems.

See Fig. 1, introduce heterogeneous wireless network multi-link parallel transmission Problems existing: the time delay arriving receiving terminal by different connection (in figure being three) due to packet is different, the data packet disorder phenomenon that result in multi-link parallel transmission system is very serious, receiving terminal must set up the buffer area that reorders, and resequences at this for packet.The packet of a certain particular number to be waited in buffer area and is numbered than it the time that little packet all arrives reordering, and is exactly the time delay that reorders of this packet.The time delay that reorders increases the end-to-end time delay of Business Stream, has had a strong impact on the performance of upper layer transport protocol.

In multi-link parallel transmission transmission, a major issue is exactly how to reduce the extra time delay that reorders.During by optimizing multi-link parallel transmission each connect between assignment of traffic ratio, effectively can reduce the time delay that reorders, be a kind of desirable route solving the delay problem that reorders.

In existing multi-link parallel transmission technology, in the flow allocation method being target with the time delay that reorders reduced between each connection, there is the method for following three kinds of better performances: the flow allocation method measured based on media interviews control MAC (MediaAccessControl) layer, based on flow allocation method and the business separation mixed method of feedback.Introduce it respectively below:

(1) based on the flow allocation method that MAC layer is measured: the method is by the propagation delay time of this connection allocation criterion as connection resource using packet, by the propagation delay time of this connection of periodic measurement, dynamic conditioning shunt ratio, make it with to be connected propagation delay time inversely proportional, thus keep the balance of load, and then reduce the time delay that reorders of receiving terminal.But the method only considered the situation that two kinds connect parallel transmission, when linking number is more than two, then inapplicable.

(2) based on the flow allocation method of feedback: the method proposes a kind of dynamic traffic distribution ratio computational methods based on receiving terminal feedback information, first, transmitting terminal periodically sends probe bag, then, according to the metrical information of receiving terminal, the data packet transmission delay of link layer is periodically returned to transmitting terminal, and calculates for shunt ratio.And the shunting ratio calculated should make the data packet transmission delay of link layer be consistent, the packet that so just can reduce receiving terminal buffer area reorders time delay.Assignment of traffic ratio in the method periodically can adjust along with the change of wireless connection capability, and the method, after ratio polymerization, effectively can reduce the time delay that reorders, but it needs longer polymerization time.

(3) mixed method of business separation: this mixed method is made up of following two kinds of methods: the optimal subset hop algorithm of the algorithm according to ideal model computed segmentation ratio and the available connection of selection based on Fuzzy Multiple Attribute Decision Making Theory, by to the contrast between the error between the ration of division theoretical value calculated and actual transmissions value and error threshold value, when error is lower than error threshold, select ideal model algorithm, and when error is higher than error threshold, only select the incompatible parallel transmission of the optimal subset of available connection.This algorithm significantly can reduce the time delay that reorders, but, because ideal model ration of division algorithm wherein and actual conditions exist deviation, this algorithm performance is in actual applications suppressed.

Summary of the invention

In view of this, the object of this invention is to provide the flow allocation method in the multi-link parallel transmission of a kind of heterogeneous wireless network, the method is in the multi-link parallel transmission of heterogeneous wireless network, the transmission performance parameter utilizing each to connect, calculate the optimization assignment of traffic ratio between each connection, and according to this optimization assignment of traffic ratio, business data flow is configured to multiple sub data flows of parallel transmission, to reorder time delay to reduce receiving terminal.Optimization flow rate ratio computational methods of the present invention are maximum delay difference MMDD (MinimizetheMaximumDelayDifference) minimized between connection is optimization aim, the propagation delay time connected by making each is close as much as possible, to reduce the time delay that reorders in the multi-link parallel transmission of heterogeneous wireless network.Because this optimization problem is NP-hard problem, the present invention proposes a kind of method for solving based on genetic annealing algorithms, the optimization assignment of traffic ratio of gained effectively can reduce the time delay that reorders, and compared with the existing methods, in average packet error rate and throughput performance, also has more advantage.

In order to achieve the above object, the invention provides the flow allocation method in the multi-link parallel transmission of a kind of heterogeneous wireless network, it is characterized in that: according to the transmission performance parameter that in heterogeneous wireless network, each connects, data estimator bag is by the different propagation delay time connected, introduce Global Genetic Simulated Annealing Algorithm GASA (GeneticAlgorithmwithSimulatedAnnealing) calculation optimization assignment of traffic ratio again, consider the maximum transmission rate of each Internet resources simultaneously, mean transit delay, multiple different performance parameters of the error rate and maximum retransmission, a kind of feasible flow allocation method is provided, to realize the assignment of traffic minimizing maximum transmitted delay inequality between each connection, described method comprises following operative step:

(1) the transmission performance parameter that in heterogeneous wireless network, each connects is tested; The content of operation of this step (1) is: the sum M measuring all available connection under current network conditions, the overall transmission rate λ of business data flow; Measure the transmission performance parameter of each available connection again: maximum transmission rate r _i, mean transit delay d _i, error rate q _iwith maximum retransmission e _i; In formula, positive integer i is that heterogeneous wireless network can by the sequence number connected, and its maximum is M;

(2) according to transmission performance parameter and the business data flow overall transmission rate of each connection, the parameters initial value optimizing assignment of traffic ratio computational methods is determined; This step (2) comprises following content of operation:

(21) according to each transmission performance parameter connected and business data flow overall transmission rate, assignment of traffic ratio result vector is determined initializing constraint be: wherein, ω _ibe i-th assignment of traffic ratio connected, and have: 0≤ω _i≤ 1 He λ is the overall transmission rate of business data flow, for ensureing system stability, distributes to i-th substream of data speed ω connected _iλ should be no more than the maximum transmission rate r of this connection _i, i.e. ω _iλ≤r _i; Because the smallest allocation unit in practical application is 1 packet, thus the overall transmission rate λ of business data flow and distribute to i-th connect substream of data speed ω _iλ can only get positive integer N, i.e. ω _iλ ∈ N, λ ∈ N;

(22) according to assignment of traffic ratio result vector constraints determine search volume U, then population scale PS, maximum iteration time GN, crossing-over rate P are set _cwith aberration rate P _m, simulated annealing initial temperature T ₀with coefficient of temperature drop r; Because being initial phase, therefore initial temperature, the i.e. T=T that current iteration number counter gn=1 and Current Temperatures are simulated annealing is set simultaneously ₀; Wherein, positive integer PS is the individuality sum in population, and positive integer GN is the maximum times of iterative computation, is used as one of end condition calculated; Crossing-over rate P _cwith aberration rate P _min P all represent probability P robability, its subscript c and m is respectively English initial of intersecting crossover and variation mutation, P _cand P _mspan be respectively [0.4,0.99] and [0.0001,0.1]; Simulated annealing initial temperature T ₀for set point, the span of coefficient of temperature drop r is (0,1);

(23) the population initial value in Global Genetic Simulated Annealing Algorithm is first determined: from the U of search volume, select PS assignment of traffic ratio result vector value at random, composition initial population P={P ₁, P ₂..., P _ps; Wherein, P is the initial of English population Population, and d in this population individual assignment of traffic ratio result vector corresponding to d population at individual; ω _diin assignment of traffic ratio result vector corresponding to d population at individual, be assigned to the ratio that i-th the substream of data speed connected accounts for the overall transmission rate of business data flow, i.e. i-th assignment of traffic ratio connected, and 1≤d≤PS, 1≤i≤M;

(3) performing based on minimizing maximum delay difference between connection is the optimization assignment of traffic ratio computational methods of optimization aim; This step (3) comprises following content of operation:

(31) from current population P, combination of two goes out Q to being used as male parent list randomly, then to the every a pair male parent group { P in this male parent list _s, P _t, respectively according to crossing-over rate P _cafter carrying out interlace operation, then according to aberration rate P _mcarry out mutation operation, obtain filial generation { C _s, C _t;

In formula, current population quantity P={P ₁, P ₂..., P _ps, subscript s and t is respectively two different male parent numberings of random selecting, and its maximum is PS, and s < t; Filial generation { C _s, C _tbe { P _s, P _tafter crossover and mutation operation, and the new individuality of generation, namely the next generation of current individual is individual; C is the initial of English filial generation Children, and with parent, namely current individual p is corresponding; Described male parent is the abbreviation of parent sample, and parent sample is all individualities in parent, because population scale is PS, therefore male parent list

(32) often couple of filial generation { C is calculated respectively _s, C _trelative to its parent { P _s, P _ttwo fitness function increment sizes: Δ f=Fit (C _s)-Fit (P _s) and Δ f=Fit (C _t)-Fit (P _t); In formula, f is that fitness function Fit (x) is write a Chinese character in simplified form; Fitness function Fit (P wherein _d) calculating comprise following content of operation:

(32A) when assignment of traffic ratio result vector is P _dtime, calculate i-th propagation delay time connected according to the following equation $T_i\left({\mathrm{\&omega;}}_{di}\right):T_i\left({\mathrm{\&omega;}}_{di}\right)=\underset{n=0}{\overset{q_i}{\&Sigma;}}\&lsqb;{\left(f\right(e_i,{\mathrm{\&omega;}}_{di}\mathrm{\&lambda;}\left)\right)}^n(1-f(e_i,{\mathrm{\&omega;}}_{di}\mathrm{\&lambda;}\left)\right)(n+1)d_i\&rsqb;+{\left(f\right(e_i,{\mathrm{\&omega;}}_{di}\mathrm{\&lambda;}\left)\right)}^{q_i+1}(q_i+1)d_i;$ In formula, λ is the overall transmission rate of business data flow; ω _difor assignment of traffic ratio result vector is P _dtime, i-th assignment of traffic ratio connected; ω _diλ is assigned to i-th the substream of data speed connected; Being assigned to i-th the substream of data speed connected is ω _dipacket Error Ratio during λ d _ibe i-th mean transit delay connected, q _iand e _ibe respectively i-th error rate and maximum retransmission connected; Positive integer i is that heterogeneous wireless network can by the sequence number connected, and its maximum is M;

(32B) when assignment of traffic ratio result vector is P _dtime, calculate the time delay difference of relative transport between any two connections in the connection of M bar $D_{diff}\left(\stackrel{\&RightArrow;}{{\mathrm{\&omega;}}_d}\right):D_{diff}\left(\stackrel{\&RightArrow;}{{\mathrm{\&omega;}}_d}\right)=\&ForAll;\left|{\mathrm{\&Delta;T}}_{ij}({\mathrm{\&omega;}}_{di},{\mathrm{\&omega;}}_{dj})\right|=\&ForAll;|T_i\left({\mathrm{\&omega;}}_{di}\right)-T_j\left({\mathrm{\&omega;}}_{dj}\right)|;$ In formula, positive integer i and j are two different sequence numbers be connected, and its maximum is M, and i < j; D is the initial of English propagation delay time Delay, and Diff is writing a Chinese character in simplified form of English difference Difference, represents relative difference herein, therefore for when assignment of traffic ratio result vector be P _dtime, two connect i and j, namely distinguish corresponding assignment of traffic ratio ω _diwith corresponding assignment of traffic ratio ω _djbetween propagation delay time difference, namely i-th connect propagation delay time T _i(ω _di) the propagation delay time T that is connected with jth bar _j(ω _dj) difference, Δ T _ij(ω _di, ω _dj) value be Arbitrary Digit, but its absolute value | Δ T _ij(ω _di, ω _dj) |>=0; Function represent the arbitrary value got in x;

(32C) P is calculated _dfitness function Fit (P _d): it is P that fitness function gets assignment of traffic ratio result vector _dtime any two connect between the inverse of maximum of relative transport time delay difference, namely wherein, function F it (x) represents the fitness function of variable x in Global Genetic Simulated Annealing Algorithm, and Fit is the initial of English fitness Fitness, and Max is the initial of English maximum Maximum, and Max (x) represents the maximum of getting x; represent that assignment of traffic ratio result vector is P _jtime any two connect between relative transport time delay difference, (x) ^-1represent the inverse of x, i.e. 1/x;

(33) judge whether each Δ f is greater than 0, and namely whether formula Δ f > 0 sets up; If so, then represent that newborn filial generation fitness function value is greater than parent, namely filial generation more conforms than parent, now accepts this offspring individual C at once _sor C _tfor current population member, and replace the corresponding individuality of parent; If not, i.e. Δ f≤0, represents that the fitness function value of filial generation is less than or equal to parent, now, does not abandon this filial generation at once, but prepares to accept it with the acceptance probability of setting and become current population member: namely first calculate C _sor C _tacceptance probability after generation is positioned at the equally distributed pseudo random number random (0,1) on interval [0,1], then judge whether inequality P (Δ f) > random (0,1) sets up; If so, this offspring individual C is then accepted _sor C _tfor the member of current population, replace corresponding parent; Otherwise, abandon this offspring individual C _sor C _t, still retain the individual P of original parent _sor P _t; Wherein, acceptance probability P (Δ f) depends on fitness increment size, and e is math constant, and T is Current Temperatures;

(34) judge whether to meet one of following two stopping criterion for iteration:

Whether (a) iterations reaches set point number, and namely current iteration number of times reaches maximum iteration time: gn > GN; Or

Whether the average fitness function difference of (b) parent and filial generation is less than convergence threshold Th, and its computing formula is: filial generation is relative to average fitness increment Fit (f (C))-Fit (f (P)) the < Th of parent;

As long as meet wherein any one stopping criterion for iteration, just terminate the computational process of this assignment of traffic ratio, export the assignment of traffic ratio result vector corresponding to individuality that fitness in current population P is maximum, then using this numerical value as optimization assignment of traffic ratio vector; If not, then subsequent step (35) is performed;

Wherein, the C={C in end condition (b) ₁, C ₂... C _psrepresent whole individualities of filial generation, P={P ₁, P ₂..., P _psrepresent whole individualities of parent, i.e. current population; Filial generation C in Fit (f (C)) does not have subscript, and represent that it is the average fitness function of filial generation, its computational methods are: similarly, the parent P in Fit (f (P)) does not have subscript yet, and represent that it is the average fitness function of parent, its computational methods are $Fit\left(f\right(P))=\underset{i=1}{\overset{ps}{\&Sigma;}}\frac{Fit\left(f\right(p_{ps}))}{PS};$

(35) because population is often evolved once with regard to a renewal Current Temperatures, also upgrade current iteration number of times simultaneously; Therefore Current Temperatures T reduces gradually with coefficient of temperature drop r, i.e. T=T × r; Current iteration number of times then increases progressively gradually: i.e. gn=gn+1; Now, execution step (31) is returned; Wherein, the interval of coefficient of temperature drop r is (0,1);

(4) transmitting terminal is according to optimization assignment of traffic ratio distribution service data flow.

In heterogeneous wireless network of the present invention, the essence of the optimization method of completing the square of the flow rate ratio of multi-link parallel transmission is a nature of nonlinear integral programming problem, therefore, this is a NP difficult problem (i.e. NP-hard problem) (non-deterministicpolynomialhard).In order to above-mentioned problem effectively can be obtained close to optimum solution, present invention employs genetic annealing algorithms GASA to solve.This is because genetic algorithm (GA) has good ability of searching optimum and the speed of solution, but its ability in Local Search is poor, and easily occurs " precocity ".And simulated annealing (SA) can find best solution, but it is easily absorbed in the trap of local optimum, and its ability of searching optimum is more weak simultaneously.So GA and SA two kinds of algorithms have very strong complementary, and both combination-GASA algorithms are the effective ways solving NP-hard problem at present.

Innovative characteristics of the present invention and key technology are: introduced by GASA algorithm in multi-link parallel transmission optimization flow allocation method first, wherein, Genetic Algorithms is set to the main frame of parallel search, and simulated annealing SA is used for the sudden change process in genetic algorithm.Namely: first use GA search overhead allocative decision space, to calculate under each allocative decision each connect between maximum delay poor, according to the fitness function of this allocative decision of maximum delay difference calculating; Again filial generation fitness function value and parent fitness function value are compared.Then, present flow rate allocation result is obtained according to the strategy of SA.When meeting algorithm end condition, the assignment of traffic scheme obtained is exactly the near-optimization result of assignment of traffic.In a word, the present invention adopts the method for solving based on genetic simulated annealing (GASA), utilizes the performance of climbing the mountain of the Implicit Parallelism of genetic algorithm and annealing algorithm, effectively can reduce computation complexity, greatly accelerate solving speed and convergence rate.In addition, by arranging rational convergence threshold value further equalization algorithm performance and elapsed time again.

The present invention is based on the optimization aim minimizing maximum delay difference between each connection, compared with the flow allocation method equal with time delay between each connection, be more suitable for the embody rule scene of heterogeneous wireless network, the method of the time delay that reorders is reduced with minimizing parallel transmission quantity, more take full advantage of available network connection resource, throughput is larger.Therefore, the present invention has good popularizing application prospect.

Accompanying drawing explanation

Fig. 1 is the structure composition schematic diagram of heterogeneous wireless network.

Fig. 2 is the flow allocation method operating procedure flow chart of multi-link parallel transmission in the heterogeneous wireless network that the present invention is based on genetic annealing algorithms.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the present invention is described in further detail.

Flow allocation method in the multi-link parallel transmission of heterogeneous wireless network of the present invention, it is the transmission performance parameter according to each connects in heterogeneous wireless network, data estimator bag is by the different propagation delay time connected, introduce Global Genetic Simulated Annealing Algorithm GASA calculation optimization assignment of traffic ratio again, consider multiple different performance parameters of the maximum transmission rate of each Internet resources, mean transit delay, the error rate and maximum retransmission simultaneously, there is provided a kind of feasible flow allocation method, to realize the assignment of traffic minimizing maximum transmitted delay inequality between each connection.

See Fig. 2, introduce the concrete operation step of the inventive method:

Step 1, each transmission performance parameter connected in test heterogeneous wireless network.The content of operation of this step is: the sum M measuring all available connection under current network conditions, the overall transmission rate λ of business data flow; Measure the transmission performance parameter of each available connection i again: maximum transmission rate r _i, mean transit delay d _i, error rate q _iwith maximum retransmission e _i; In formula, positive integer i is that heterogeneous wireless network can by the sequence number connected, and its maximum is M.

Step 2, according to transmission performance parameter and the business data flow overall transmission rate of each connection, determines the parameters initial value optimizing assignment of traffic ratio computational methods.

This step comprises following content of operation:

(21) according to each transmission performance parameter connected and business data flow overall transmission rate, assignment of traffic ratio result vector is determined initializing constraint be: wherein, ω _ibe i-th assignment of traffic ratio connected, and have: 0≤ω _i≤ 1 He λ is the overall transmission rate of business data flow, for ensureing system stability, distributes to i-th substream of data speed ω connected _iλ should be no more than the maximum transmission rate r of this available connection _i, i.e. ω _iλ≤r _i; Because the smallest allocation unit in practical application is 1 packet, thus the overall transmission rate λ of business data flow and distribute to i-th connect substream of data speed ω _iλ can only get positive integer N, i.e. ω _iλ ∈ N, λ ∈ N.

(22) according to assignment of traffic ratio result vector constraints determine search volume U, then population scale PS, maximum iteration time GN, crossing-over rate P are set _cwith aberration rate P _m, simulated annealing initial temperature T ₀with coefficient of temperature drop r.

Because being initial phase, therefore initial temperature, the i.e. T=T that current iteration number counter gn=1 and Current Temperatures are simulated annealing is set simultaneously ₀.Wherein, positive integer PS is the individuality sum in population, and positive integer GN is the maximum times of iterative computation, is used as one of end condition calculated; Crossing-over rate P _cwith aberration rate P _min P all represent probability P robability, its subscript c and m is respectively English initial of intersecting crossover and variation mutation, P _cand P _mspan be respectively [0.4,0.99] and [0.0001,0.1]; Simulated annealing initial temperature T ₀for set point, the span of coefficient of temperature drop r is (0,1).

(23) the population initial value in Global Genetic Simulated Annealing Algorithm is first determined: from the U of search volume, select PS assignment of traffic ratio result vector value at random, composition initial population P={P ₁, P ₂..., P _ps.Wherein, P is the initial of English population Population, and d in this population individual assignment of traffic ratio result vector corresponding to d population at individual; ω _diin assignment of traffic ratio result vector corresponding to d population at individual, be assigned to the ratio that i-th the substream of data speed connected accounts for the overall transmission rate of business data flow, i.e. i-th assignment of traffic ratio connected, and 1≤d≤PS, 1≤i≤M.

Step 3, performing based on minimizing maximum delay difference between each connection is the optimization assignment of traffic ratio computational methods of optimization aim.This step comprises following content of operation:

(31) from current population P, combination of two goes out Q to being used as male parent list randomly, to the every a pair male parent group { P in this male parent list _s, P _t, respectively according to crossing-over rate P _cafter carrying out interlace operation, then according to aberration rate P _mcarry out mutation operation, obtain filial generation { C _s, C _t.

In formula, current population quantity P={P ₁, P ₂..., P _ps, subscript s and t is respectively two different male parent numberings of random selecting, and its maximum is PS, and s < t.Filial generation { C _s, C _tbe { P _s, P _tafter crossover and mutation operation, and the new individuality of generation, namely the next generation of current individual, parent is individual; C is the initial of English filial generation Children, and with current individual, namely parent p is corresponding.Male parent is the abbreviation of parent sample, and parent sample is all individualities in parent, because population scale is PS, therefore male parent list

(32) often couple of filial generation { C is calculated respectively _s, C _trelative to its parent { P _s, P _ttwo fitness function increment sizes: Δ f=Fit (C _s)-Fit (P _s) and Δ f=Fit (C _t)-Fit (P _t); In formula, f is that fitness function Fit (x) is write a Chinese character in simplified form.

This fitness function Fit (P _d) calculating be emphasis, its computational methods can be divided into following three content of operation:

(32A) when assignment of traffic ratio result vector is P _dtime, calculate i-th propagation delay time T connected according to the following equation _i(ω _di):

$T_i\left({\mathrm{\&omega;}}_{di}\right)=\underset{n=0}{\overset{q_i}{\mathrm{\&Sigma;}}}\&lsqb;{\left(f\right(e_i,{\mathrm{\&omega;}}_{di}\mathrm{\&lambda;}\left)\right)}^n(1-f(e_i,{\mathrm{\&omega;}}_{di}\mathrm{\&lambda;}\left)\right)(n+1)d_i\&rsqb;+{\left(f\right(e_i,{\mathrm{\&omega;}}_{ji}\mathrm{\&lambda;}\left)\right)}^{q_i+1}(q_i+1)d_i;$ In formula, λ is the overall transmission rate (unit is packet/s) of business data flow; ω _difor assignment of traffic ratio result vector is P _dtime, i-th assignment of traffic ratio connected; ω _diλ is assigned to i-th the substream of data speed connected; Being assigned to i-th the substream of data speed connected is ω _dipacket Error Ratio during λ d _ibe i-th mean transit delay connected, q _iand e _ibe respectively i-th error rate and maximum retransmission connected; Positive integer i is the sequence number that heterogeneous wireless network connects, and its maximum is M.

(32B) when assignment of traffic ratio result vector is P _dtime, calculate the time delay difference of relative transport between any two connections in the connection of M bar $D_{diff}\left(\stackrel{\&RightArrow;}{{\mathrm{\&omega;}}_d}\right):D_{diff}\left(\stackrel{\&RightArrow;}{{\mathrm{\&omega;}}_d}\right)=\&ForAll;\left|{\mathrm{\&Delta;T}}_{ij}({\mathrm{\&omega;}}_{di},{\mathrm{\&omega;}}_{dj})\right|=\&ForAll;|T_i\left({\mathrm{\&omega;}}_{di}\right)-T_j\left({\mathrm{\&omega;}}_{dj}\right)|.$

In formula, positive integer i and j are two different sequence numbers be connected, and its maximum is M, and i < j; D is the initial of English propagation delay time Delay, and Diff is writing a Chinese character in simplified form of English difference Difference, represents relative difference herein, therefore Δ represents the difference of two numbers, Δ T _ij(ω _di, ω _dj) be when assignment of traffic ratio result vector is P _dtime, two connect i and j, namely distinguish corresponding assignment of traffic ratio ω _diwith corresponding assignment of traffic ratio ω _djbetween propagation delay time difference, namely i-th connect propagation delay time T _i(ω _di) the propagation delay time T that is connected with jth bar _j(ω _dj) difference, Δ T _ij(ω _di, ω _dj) value be Arbitrary Digit, but its absolute value | Δ T _ij(ω _di, ω _dj) |>=0.Function represent the arbitrary value got in x.

(32C) P is calculated _dfitness function Fit (P _d): it is P that fitness function gets assignment of traffic ratio result vector _dtime any two connect between the inverse of maximum of relative transport time delay difference, namely $Fit\left(P_d\right)={\left(Max\right(D_{diff}\left(\stackrel{\&RightArrow;}{{\mathrm{\&omega;}}_d}\right)\left)\right)}^{-1}.$

Wherein, function F it (x) represents the fitness function of variable x in Global Genetic Simulated Annealing Algorithm, and Fit is the initial of English fitness Fitness, and Max is the initial of English maximum Maximum, and Max (x) represents the maximum of getting x; represent that assignment of traffic ratio result vector is P _jtime any two connect between relative transport time delay difference, (x) ^-1represent the inverse of x, i.e. 1/x.

It should be noted that: above with P _dfitness function Fit (P in this step is described for example _d) computational methods, and Fit (C _d) computational methods identical therewith, its difference only need change variable P _dfor variable C _d.

(33) judge whether each Δ f is greater than 0, and namely whether formula Δ f > 0 sets up; If so, then represent that newborn filial generation fitness function value is greater than parent, namely filial generation more conforms than parent, now accepts this offspring individual C at once _sor C _tfor current population member, and replace the corresponding individuality of parent; If not, i.e. Δ f≤0, represents that the fitness function value of filial generation is less than or equal to parent, now, does not abandon this filial generation at once, but prepares to accept it with the acceptance probability of setting and become current population member: namely first calculate C _sor C _tacceptance probability after generation is positioned at the equally distributed pseudo random number random (0,1) on interval [0,1], then judge whether inequality P (Δ f) > random (0,1) sets up; If so, this filial generation C is then accepted _sor C _tfor the member of current population, replace corresponding parent; Otherwise, abandon this new offspring individual, still retain the individual P of original parent _sor P _t; Wherein, acceptance probability P (Δ f) depends on fitness increment size, and e is math constant, and T is Current Temperatures.

In this step (33), because time initial, temperature T is higher, therefore numerical value close to 1, have larger probability to accept the poor filial generation of fitness value.But, along with the reduction of Current Temperatures T, numerical value constantly diminish, finally almost no longer accept the poor filial generation of fitness function value.This acceptance probability is adopted as receiving the tactful benefit of filial generation to be: avoid search procedure to be absorbed in the trap of local optimum prematurely, can obtain the optimization assignment of traffic ratio vector of the overall situation with larger probability, this operating procedure embodies the thought of simulated annealing.

(34) judge whether to meet one of following two stopping criterion for iteration:

Whether (a) iterations reaches set point number, and namely current iteration number of times reaches maximum iteration time: gn > GN; Or

Whether the average fitness function difference of (b) parent and filial generation is less than convergence threshold Th, and its computing formula is: filial generation is relative to average fitness increment Fit (f (C))-Fit (f (P)) the < Th of parent;

As long as meet wherein any one stopping criterion for iteration, just terminate the computational process of this assignment of traffic ratio, export the assignment of traffic ratio result vector corresponding to individuality that fitness in current population P is maximum, then using this numerical value as optimization assignment of traffic ratio vector; If not, then subsequent step (35) is performed;

Wherein, the C={C in end condition (b) ₁, C ₂... C _psrepresent whole individualities of filial generation, P={P ₁, P ₂..., P _psrepresent parent (i.e. current population) whole individualities; Filial generation C in Fit (f (C)) does not have subscript, and represent that it is the average fitness function of filial generation, its computational methods are: similarly, the parent P in Fit (f (P)) does not have subscript yet, and represent that it is the average fitness function of parent, its computational methods are $Fit\left(f\right(P\left)\right)=\underset{i=1}{\overset{ps}{\&Sigma;}}\frac{Fit\left(f\right(P_{ps}\left)\right)}{PS}.$

In this step, it is 4 ~ 6% that the present invention arranges convergence threshold Th, when the average fitness increment of population is less than 4 ~ 6%, and termination of iterations calculating operation, export the assignment of traffic ratio result vector corresponding to individuality that in current population, fitness function value is maximum, as optimization assignment of traffic ratio.Meanwhile, for preventing computing time long, greatest iteration algebraically GN is set for auxiliary end condition, namely when current iteration number of times gn is more than GN, also iterative computation operation is stopped, the assignment of traffic ratio result vector corresponding to individuality that in the current population of same output, fitness function value is maximum, as optimization assignment of traffic ratio.

(35) because population is often evolved once with regard to a renewal Current Temperatures, also upgrade current iteration number of times simultaneously; Therefore Current Temperatures T reduces gradually with coefficient of temperature drop r, i.e. T=T × r; Current iteration number of times then increases progressively gradually: i.e. gn=gn+1; Now, execution step (31) is returned; Wherein, adopt index cooling strategy, the interval of coefficient of temperature drop r is (0,1).

Step 4, transmitting terminal is according to optimization assignment of traffic ratio distribution service data flow.This step comprises following content of operation: business data packet stream, in transmission buffer area, is divided into multiple packet subflow according to the optimization assignment of traffic ratio calculated by transmitting terminal, and by different connection parallel transmissions to receiving terminal; Packet arrive receiving terminal time, because there is data packet disorder phenomenon, thus these packets be introduced in the buffer area that reorders wait for reorder, then receiving end receive.

Inventions have been the test of Multi simulation running embodiment, the result of test is successful, achieves goal of the invention.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within the scope of protection of the invention.

Claims (5)

1. the flow allocation method in the multi-link parallel transmission of heterogeneous wireless network, it is characterized in that: according to the transmission performance parameter that in heterogeneous wireless network, each connects, data estimator bag is by the different propagation delay time connected, introduce Global Genetic Simulated Annealing Algorithm GASA calculation optimization assignment of traffic ratio again, consider the maximum transmission rate of each Internet resources simultaneously, mean transit delay, multiple different performance parameters of the error rate and maximum retransmission, a kind of feasible flow allocation method is provided, to realize the assignment of traffic minimizing maximum transmitted delay inequality between each connection, described method comprises following operative step:

(1) the transmission performance parameter that in heterogeneous wireless network, each connects is tested; The content of operation of this step (1) is: the sum M measuring all available connection under current network conditions, the overall transmission rate λ of business data flow; Measure the transmission performance parameter of each available connection again: maximum transmission rate r _i, mean transit delay d _i, error rate q _iwith maximum retransmission e _i; In formula, positive integer i is that heterogeneous wireless network can by the sequence number connected, and its maximum is M;

(2) according to transmission performance parameter and the business data flow overall transmission rate of each connection, the parameters initial value optimizing assignment of traffic ratio computational methods is determined; This step (2) comprises following content of operation:

(21) according to each transmission performance parameter connected and business data flow overall transmission rate, assignment of traffic ratio result vector is determined initializing constraint be: wherein, ω _ibe i-th assignment of traffic ratio connected, and have: 0≤ω _i≤ 1 He λ is the overall transmission rate of business data flow, for ensureing system stability, distributes to i-th substream of data speed ω connected _iλ should be no more than the maximum transmission rate r of this connection _i, i.e. ω _iλ≤r _i; Because the smallest allocation unit in practical application is 1 packet, thus the overall transmission rate λ of business data flow and distribute to i-th connect substream of data speed ω _iλ can only get positive integer N, i.e. ω _iλ ∈ N, λ ∈ N;

(22) according to assignment of traffic ratio result vector constraints determine search volume U, then population scale PS, maximum iteration time GN, crossing-over rate P are set _cwith aberration rate P _m, simulated annealing initial temperature T ₀with coefficient of temperature drop r; Because being initial phase, therefore initial temperature, the i.e. T=T that current iteration number counter gn=1 and Current Temperatures are simulated annealing is set simultaneously ₀; Wherein, positive integer PS is the individuality sum in population, and positive integer GN is the maximum times of iterative computation, is used as one of end condition calculated; Crossing-over rate P _cwith aberration rate P _min P all represent probability P robability, its subscript c and m is respectively English initial of intersecting crossover and variation mutation, P _cand P _mspan be respectively [0.4,0.99] and [0.0001,0.1]; Simulated annealing initial temperature T ₀for set point, the span of coefficient of temperature drop r is (0,1);

(23) the population initial value in Global Genetic Simulated Annealing Algorithm is first determined: from the U of search volume, select PS assignment of traffic ratio result vector value at random, composition initial population P={P ₁, P ₂..., P _ps; Wherein, P is the initial of English population Population, and d in this population individual assignment of traffic ratio result vector corresponding to d population at individual; ω _diin assignment of traffic ratio result vector corresponding to d population at individual, be assigned to the ratio that i-th the substream of data speed connected accounts for the overall transmission rate of business data flow, i.e. i-th assignment of traffic ratio connected, and 1≤d≤PS, 1≤i≤M;

(3) performing based on minimizing maximum delay difference between connection is the optimization assignment of traffic ratio computational methods of optimization aim; This step (3) comprises following content of operation:

(31) from current population P, combination of two goes out Q to being used as male parent list randomly, then to the every a pair male parent group { P in this male parent list _s, P _t, respectively according to crossing-over rate P _cafter carrying out interlace operation, then according to aberration rate P _mcarry out mutation operation, obtain filial generation { C _s, C _t;

In formula, current population quantity P={P ₁, P ₂..., P _ps, subscript s and t is respectively two different male parent numberings of random selecting, and its maximum is PS, and s < t; Filial generation { C _s, C _tbe { P _s, P _tafter crossover and mutation operation, and the new individuality of generation, namely the next generation of current individual is individual; C is the initial of English filial generation Children, and with parent, namely current individual p is corresponding; Described male parent is the abbreviation of parent sample, and parent sample is all individualities in parent, because population scale is PS, therefore male parent list

(32) often couple of filial generation { C is calculated respectively _s, C _trelative to its parent { P _s, P _ttwo fitness function increment sizes: Δ f=Fit (C _s)-Fit (P _s) and Δ f=Fit (C _t)-Fit (P _t); In formula, f is that fitness function Fit (x) is write a Chinese character in simplified form; Fitness function Fit (P wherein _d) calculating comprise following content of operation:

(32A) when assignment of traffic ratio result vector is P _dtime, calculate i-th propagation delay time connected according to the following equation in formula, λ is the overall transmission rate of business data flow; ω _difor assignment of traffic ratio result vector is P _dtime, i-th assignment of traffic ratio connected; ω _diλ is assigned to i-th the substream of data speed connected; Being assigned to i-th the substream of data speed connected is ω _dipacket Error Ratio during λ d _ibe i-th mean transit delay connected, q _iand e _ibe respectively i-th error rate and maximum retransmission connected; Positive integer i is that heterogeneous wireless network can by the sequence number connected, and its maximum is M;

(32B) when assignment of traffic ratio result vector is P _dtime, calculate the time delay difference of relative transport between any two connections in the connection of M bar in formula, positive integer i and j are two different sequence numbers be connected, and its maximum is M, and i < j; D is the initial of English propagation delay time Delay, and Diff is writing a Chinese character in simplified form of English difference Difference, represents relative difference herein, therefore Δ T _ij(ω _di, ω _dj) be when assignment of traffic ratio result vector is P _dtime, two connect i and j, namely distinguish corresponding assignment of traffic ratio ω _diwith corresponding assignment of traffic ratio ω _djbetween propagation delay time difference, namely i-th connect propagation delay time T _i(ω _di) the propagation delay time T that is connected with jth bar _j(ω _dj) difference, Δ T _ij(ω _di, ω _dj) value be Arbitrary Digit, but its absolute value | Δ T _ij(ω _di, ω _dj) |>=0; Function represent the arbitrary value got in x;

(32C) P is calculated _dfitness function Fit (P _d): it is P that fitness function gets assignment of traffic ratio result vector _dtime any two connect between the inverse of maximum of relative transport time delay difference, namely wherein, function F it (x) represents the fitness function of variable x in Global Genetic Simulated Annealing Algorithm, and Fit is the initial of English fitness Fitness, and Max is the initial of English maximum Maximum, and Max (x) represents the maximum of getting x; represent that assignment of traffic ratio result vector is P _jtime any two connect between relative transport time delay difference, (x) ^-1represent the inverse of x, i.e. 1/x;

(33) judge whether each Δ f is greater than 0, and namely whether formula Δ f > 0 sets up; If so, then represent that newborn filial generation fitness function value is greater than parent, namely filial generation more conforms than parent, now accepts this offspring individual C at once _sor C _tfor current population member, and replace the corresponding individuality of parent; If not, i.e. Δ f≤0, represents that the fitness function value of filial generation is less than or equal to parent, now, does not abandon this filial generation at once, but prepares to accept it with the acceptance probability of setting and become current population member: namely first calculate C _sor C _tacceptance probability after generation is positioned at the equally distributed pseudo random number random (0,1) on interval [0,1], then judge whether inequality P (Δ f) > random (0,1) sets up; If so, this offspring individual C is then accepted _sor C _tfor the member of current population, replace corresponding parent; Otherwise, abandon this offspring individual C _sor C _t, still retain the individual P of original parent _sor P _t; Wherein, acceptance probability P (Δ f) depends on fitness increment size, and e is math constant, and T is Current Temperatures;

(34) judge whether to meet one of following two stopping criterion for iteration:

Whether (a) iterations reaches set point number, and namely current iteration number of times reaches maximum iteration time: gn > GN; Or

Whether the average fitness function difference of (b) parent and filial generation is less than convergence threshold Th, and its computing formula is: filial generation is relative to average fitness increment Fit (f (C))-Fit (f (P)) the < Th of parent;

As long as meet wherein any one stopping criterion for iteration, just terminate the computational process of this assignment of traffic ratio, export the assignment of traffic ratio result vector corresponding to individuality that fitness in current population P is maximum, then using this numerical value as optimization assignment of traffic ratio vector; If not, then subsequent step (35) is performed;

Wherein, the C={C in end condition (b) ₁, C ₂... C _psrepresent whole individualities of filial generation, P={P ₁, P ₂..., P _psrepresent whole individualities of parent, i.e. current population; Filial generation C in Fit (f (C)) does not have subscript, and represent that it is the average fitness function of filial generation, its computational methods are: similarly, the parent P in Fit (f (P)) does not have subscript yet, and represent that it is the average fitness function of parent, its computational methods are

(35) because population is often evolved once with regard to a renewal Current Temperatures, also upgrade current iteration number of times simultaneously; Therefore Current Temperatures T reduces gradually with coefficient of temperature drop r, i.e. T=T × r; Current iteration number of times then increases progressively gradually: i.e. gn=gn+1; Now, execution step (31) is returned; Wherein, the interval of coefficient of temperature drop r is (0,1);

(4) transmitting terminal is according to optimization assignment of traffic ratio distribution service data flow.

2. method according to claim 1, is characterized in that: the fitness function Fit (C in described step (32) _d) calculating operation content and Fit (P _d) calculating operation content identical, its difference is just by corresponding variable P _dbe replaced by variable C _d.

3. method according to claim 1, is characterized in that: in described step (33), because temperature T time initial is higher, therefore numerical value close to 1, then have larger probability to accept the poor filial generation of fitness value; But, along with the reduction of Current Temperatures T, numerical value constantly diminish, finally almost no longer accept the poor filial generation of fitness function value; This acceptance probability is adopted as receiving the tactful benefit of filial generation to be: avoid search procedure to be absorbed in the trap of local optimum prematurely, can obtain the optimization assignment of traffic ratio vector of the overall situation with larger probability, this operating procedure embodies the thought of simulated annealing.

4. method according to claim 1, it is characterized in that: in described step (34), arranging convergence threshold Th is 4 ~ 6%, when the average fitness increment of population is less than 4 ~ 6%, termination of iterations calculating operation, export the assignment of traffic ratio result vector corresponding to individuality that in current population, fitness function value is maximum, as optimization assignment of traffic ratio; Simultaneously, for preventing computing time long, greatest iteration algebraically GN is set for auxiliary end condition, namely when current iteration number of times gn is more than GN, also iterative computation operation is stopped, the assignment of traffic ratio result vector corresponding to individuality that in the current population of same output, fitness function value is maximum, as optimization assignment of traffic ratio.

5. method according to claim 1, it is characterized in that: described step (4) comprises following content of operation: transmitting terminal is in transmission buffer area, according to the optimization assignment of traffic ratio calculated, business data packet stream is divided into multiple packet subflow, and by different connection parallel transmissions to receiving terminal; Packet arrive receiving terminal time, because there is data packet disorder phenomenon, thus these packets be introduced in the buffer area that reorders wait for reorder, then receiving end receive.