CN102523585A - Cognitive radio method based on improved genetic algorithm - Google Patents

Abstract

The invention discloses a cognitive radio method based on an improved genetic algorithm in the technical field of radio communication. The cognitive radio method comprises the steps of: setting initial parameters of cognitive radio and using the initial parameters as chromosomes of the genetic algorithm; setting initial mutation probability, population size and maximum evolutional generations, and setting target functions which reflect current link quality and the weight of each target function; calculating a population fitness value; conducting scale transformation of the fitness value; selecting the chromosomes; using self-adaptive crossover probability and mutation probability to conduct two-point crossover and individual mutation on the chromosomes; judging whether a condition of convergence is reached or not, and if not, returning to calculate the population fitness value; and if so, outputting a result set and using the results as the parameters of the cognitive radio. The cognitive radio method solves the problem that the final solution set is apt to be converged into a locally optimal solution in the genetic algorithm, and guarantees the diversity of populations and the convergence of the genetic algorithm at the same time.

Description

Based on the cognitive radio approaches of improving genetic algorithm

Technical field

The invention belongs to wireless communication technology field, relate in particular to a kind of based on the cognitive radio approaches of improving genetic algorithm.

Background technology

The sustainable growth of radio communication service demand causes wireless communication system that the demand of frequency spectrum resource is constantly increased, to such an extent as to that radio spectrum resources becomes is more and more rare.Yet spectrum measurement research shows, authorizes the utilization rate of frequency spectrum very low, authorizes the waste of frequency spectrum hole serious, and static spectrum allocation may system does not match with dynamic spectrum utilization mode.The basic ideas that address this problem are to improve the existing availability of frequency spectrum as far as possible.Cognitive radio technology has also just arisen at the historic moment as the technology that can satisfy this requirement.

Cognitive radio (Cognitive Radio; CR) notion is proposed in 1999 by Joseph doctor Mitolo, and its core concept is that CR has learning ability, can with the surrounding environment interactive information; With perception be utilized in the usable spectrum in this space, and restriction and reduce the generation of conflict.The purpose of cognitive radio is not influence on the basis of authorizing the frequency range proper communication, making in the frequency range of Wireless Telecom Equipment according to " chance access way " insertion authority with cognitive function, dynamically utilizing frequency spectrum, solves the nervous problem of frequency spectrum.

Cognitive radio has two main features: cognitive ability with reshuffle ability.Wherein reshuffle part and need carry out parameter adjustment; And parameter adjustment must be satisfied many-sided requirements such as channel condition, user's request and system qualification; This just needs cognitive radio between a plurality of conditions, to weigh, thereby obtains one group of better parameter configuration scheme.Genetic algorithm simulation biological evolution mechanism can be carried out global search in the optimizing space, have the unexistent multiple-objection optimization ability of general algorithm.But simple generic algorithm is not controlled chromosome, if occur the high individuality of fitness in the population, is very easy to obtain locally optimal solution.In addition, simple generic algorithm uses fixing crossover probability and variation probability, defect individual is not protected, and relatively poor individuality is suppressed, and does not also treat with a certain discrimination according to the different situations of phase before and after evolving, can influence performance.

In order to address the above problem, must the simple generic algorithm that parameter adjustment is partly used be improved, to obtain preferable performance.。

Summary of the invention

The objective of the invention is to, provide a kind of, in order to solve the problem that the cognitive radio approaches based on genetic algorithm commonly used exists based on the cognitive radio approaches of improving genetic algorithm.

To achieve these goals, technical scheme provided by the invention is that a kind of cognitive radio approaches based on the improvement genetic algorithm is characterized in that said method comprises:

Step 1: set the initial parameter of cognitive radio, with its chromosome as genetic algorithm;

Step 2: set initial variation probability, population size and maximum evolutionary generation, and set the target function of the current link-quality of reflection and the weight of each target function;

Step 3: calculate the population fitness value;

Step 4: the change of scale that carries out fitness value; The mean value of the fitness value behind the assurance change of scale equals the mean value of the fitness value before the change of scale, and the maximum of the fitness value behind the change of scale equals the setting multiple of the mean value of the preceding fitness value of change of scale;

Step 5: selective staining body;

Step 6: use adaptive crossover probability chromosome to be carried out at 2 and intersect and individual variation with the variation probability;

Step 7: judge whether to reach one of following two conditions of convergence, if reach one of following two conditions of convergence, then execution in step 8; Otherwise, return step 3;

The said condition of convergence is:

1) evolutionary generation surpasses the maximum evolutionary generation of setting;

2) the individual maximum adaptation degree of population value is more than or equal to set point;

Step 8: the output result set, with its parameter as cognitive radio.

Said step 3 is specifically:

Step 31: appoint and get two individuals in the population, it is individual j and individual k respectively;

Step 32: more individual j and the performance of individual k on i target function, if the performance of individual j on i target function is better than the performance of individual k on i target function, then make comparison function c (i, j, k)=1; Otherwise make comparison function c (i, j, k)=0;

Step 33: calculate the order of individual j on i target function; Its computing formula is:

wherein; J=1 ..., m; K=1; ..., m, m are the individual amount in the population;

Step 34: the fitness value that calculates the individual j of population; Computing formula is

wherein, i=1,2; ...; N, n are the number of target function, and w (i) is the weight of i target function.

Said step 5 is specifically:

Step 51: calculate all individual fitness value sums, be designated as S;

Step 52: from (0, choose random number in S), be designated as q;

Step 53: in population, since the 0 ideal adaptation degree value that adds up, the result who adds up is designated as s, when s＞q, stop to add up and individuality that will this moment as the chromosome of selecting.

The computing formula of said adaptive crossover probability is:

$P_c=\left\{\begin{array}{c}{P}_{c1}-\frac{(P_{c1}-P_{c2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{c1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.$

Wherein, P _cBe adaptive crossover probability, P _C1And P _C2Be respectively the upper and lower bound of crossover probability, f ' is the greater of the fitness value of two individuals that will intersect, f _MaxBe the maximum of population fitness value, f _AvgMean value for the population fitness value.

The computing formula of said adaptive variation probability is:

$P_m=\left\{\begin{array}{c}{P}_{m1}-\frac{(P_{m1}-P_{m2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{m1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.$

Wherein, P _mBe adaptive variation probability, P _M1And P _M2Be respectively the upper and lower bound of variation probability, f ' is the greater of the fitness value of two individuals that will intersect, f _MaxBe the maximum of population fitness value, f _AvgMean value for the population fitness value.

The invention solves in the genetic algorithm last disaggregation that the heredity deception causes and converge on the problem of locally optimal solution easily; In intersecting and making a variation, use adaptive crossover probability and variation probability simultaneously, not only kept the diversity of population but also guaranteed convergence of genetic algorithm.

Description of drawings

Fig. 1 is based on the cognitive radio approaches flow chart that improves genetic algorithm.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit scope of the present invention and application thereof.

Fig. 1 is based on the cognitive radio approaches flow chart that improves genetic algorithm.Among Fig. 1, the cognitive radio approaches based on the improvement genetic algorithm provided by the invention comprises:

Step 1: set the initial parameter of cognitive radio,, connect to form cognitive radio chromosome with these parameters such as power, modulation system, rolloff-factor, character rate etc.Set each parameter value scope then, and, finally confirm chromosome size according to the definite coding figure place of using of this scope.

Step 2: set initial variation probability, population size and maximum evolutionary generation, and set the target function of the current link-quality of reflection, like average transmit power, data rate, the error rate, bandwidth, channel efficiency, packet time-delay etc.According to difference,, set the weight of each target function simultaneously just to the difference of target function demand to communicating requirement.

Step 3: calculate the population fitness value.

At first, appoint and get two individuals in the population, it is individual j and individual k respectively.

Then, more individual j and the performance of individual k on i target function, when individual j has better performance than individual k on i target function, make comparison function c (i, j, k)=1, otherwise c (i, j, k)=0.

Next, calculating the order of individual j on i target function is:

$r(i,j)=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}c(i,j,k),(j=1,...,m,k=1,...,m)---\left(1\right)$

Wherein, j=1 ..., m, k=1 ..., m, m are the individual amount in the population.Because what fitness function adopted is comparison function, what promptly fitness value was represented is that corresponding individuality is superior to other individual comparand value in the population, so must select fixing object of reference, selecting initial population here is object of reference.What use in the step therefore, is " relative adaptation degree ".

At last, consider the target function weight, the final fitness of individual j is:

$r\left(j\right)=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}w\left(i\right)\&times;r(i,j)+1,(j=1,...,m)---\left(2\right)$

Wherein, i=1,2 ..., n, n are the number of target function, w (i) is the weight of i target function.Below carrying out, before the step, can the chromosomal fitness value that does not satisfy constraints be put 0, get rid of these individualities and evolved to follow-on possibility.

Step 4: the change of scale that carries out fitness value.

Adopt linear transformation in the present embodiment, suppose that former fitness function is f, fitness function is f ' after the conversion, and linear change then is expressed as: f '=af+b.The value of a and b should satisfy: original fitness mean value f _AvgEqual fitness draw value f ' after the conversion _Avg(guaranteeing that fitness is that the individuality of mean value is 1 at follow-on desired replication number).In addition, also should satisfy fitness maximum f ' after the conversion _MaxBe f _AvgSpecified multiple C _Mult(individuality of control fitness maximum is at the follow-on number that duplicates), C _MultIt is the number that duplicates for the optimum individual in the population that obtains expecting.

Step 5: selective staining body.

Present embodiment uses roulette back-and-forth method selective staining body, and its concrete steps are:

Step 51: calculate all individual fitness value sums in the population, be designated as S.

Step 52: from (0, choose random number in S), be designated as q.

Step 53: in population, since the 0 ideal adaptation degree value that adds up, the result who adds up is designated as s, when s＞q, stop to add up and individuality that will this moment as the chromosome of selecting.(for each population, step 51 is only carried out once)

Step 6: use adaptive crossover probability chromosome to be carried out at 2 and intersect and individual variation with the variation probability.

Using adaptive crossover probability with the variation probability chromosome to be carried out at 2 intersects and individual variation.Adaptive crossover probability P _cWith the variation probability P _m, can change automatically with fitness.When population individuals fitness reaches unanimity or is tending towards local optimum, P _cAnd P _mIncrease, and when colony's fitness relatively disperses, P _cAnd P _mReduce.Simultaneously, fitness is higher than the individuality of the average fitness value of colony, corresponding to lower P _cAnd P _m, this is separated is protected the entering next generation; And be lower than the individuality of average fitness value, corresponding to higher P _cAnd P _m, this is separated is eliminated.Adaptive genetic algorithm can keep convergence of genetic algorithm simultaneously keeping the multifarious while of colony.

Adaptive P _cAnd P _mComputational methods are following:

$P_c=\left\{\begin{array}{c}{P}_{c1}-\frac{(P_{c1}-P_{c2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{c1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(3\right)$

$P_m=\left\{\begin{array}{c}{P}_{m1}-\frac{(P_{m1}-P_{m2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{m1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.---\left(4\right)$

P wherein _C1, P _C2, P _M1And P _M2Be respectively the bound of crossover probability and variation probability, can rule of thumb set, one group of value of recommending can be 0.9,0.6,0.1 and 0.001.

Step 7: judge whether to reach one of following two conditions of convergence, if reach one of following two conditions of convergence, then execution in step 8; Otherwise, return step 3.

The said condition of convergence is:

1) evolutionary generation surpasses the maximum evolutionary generation of setting;

2) the individual maximum adaptation degree of population value is more than or equal to set point.

For fear of going down because of not obtaining the unconfined computing of convergence solution, maximum evolutionary generation can rule of thumb be set, it should be not too high, but should guarantee that maximum adaptation degree value can restrain.When obtaining to satisfy the maximum adaptation degree value of user expectation, even if also do not reach convergency value this moment, also need not rerun down in addition, therefore, also will set the fitness value upper limit, this can obtain through formula (1) and formula (2).So when satisfying one of them of following two conditions of convergence, promptly stop computing, return results.The condition of convergence is:

1) evolutionary generation surpasses the maximum evolutionary generation of setting;

2) the individual maximum adaptation degree of population value is more than or equal to set point.

Step 8: the output result set, with its parameter as cognitive radio.

The present invention is through above-mentioned step, solved the problem that the heredity deception causes in the genetic algorithm last disaggregation converges on locally optimal solution easily.In intersection and variation, used adaptive crossover probability and variation probability, can separate each like this provides relatively better intersection and variation, can keep the multifarious while of population, guarantees convergence of genetic algorithm.

The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technical staff who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (5)

1. one kind based on the cognitive radio approaches of improving genetic algorithm, it is characterized in that said method comprises:

Step 1: set the initial parameter of cognitive radio, with its chromosome as genetic algorithm;

Step 2: set initial variation probability, population size and maximum evolutionary generation, and set the target function of the current link-quality of reflection and the weight of each target function;

Step 3: calculate the population fitness value;

Step 4: the change of scale that carries out fitness value; The mean value of the fitness value behind the assurance change of scale equals the mean value of the fitness value before the change of scale, and the maximum of the fitness value behind the change of scale equals the setting multiple of the mean value of the preceding fitness value of change of scale;

Step 5: selective staining body;

Step 6: use adaptive crossover probability chromosome to be carried out at 2 and intersect and individual variation with the variation probability;

Step 7: judge whether to reach one of following two conditions of convergence, if reach one of following two conditions of convergence, then execution in step 8; Otherwise, return step 3;

The said condition of convergence is:

1) evolutionary generation surpasses the maximum evolutionary generation of setting;

2) the individual maximum adaptation degree of population value is more than or equal to set point;

Step 8: the output result set, with its parameter as cognitive radio.

2. according to claim 1 a kind of based on the cognitive radio approaches of improving genetic algorithm, it is characterized in that said step 3 specifically:

Step 31: appoint and get two individuals in the population, it is individual j and individual k respectively;

Step 32: more individual j and the performance of individual k on i target function, if the performance of individual j on i target function is better than the performance of individual k on i target function, then make comparison function c (i, j, k)=1; Otherwise make comparison function c (i, j, k)=0;

Step 33: calculate the order of individual j on i target function; Its computing formula is:

wherein; J=1 ..., m; K=1; ..., m, m are the individual amount in the population;

Step 34: the fitness value that calculates the individual j of population; Computing formula is

wherein, i=1,2; ...; N, n are the number of target function, and w (i) is the weight of i target function.

3. according to claim 1 a kind of based on the cognitive radio approaches of improving genetic algorithm, it is characterized in that said step 5 specifically:

Step 51: calculate all individual fitness value sums, be designated as S;

Step 52: from (0, choose random number in S), be designated as q;

Step 53: in population, since the 0 ideal adaptation degree value that adds up, the result who adds up is designated as s, when s＞q, stop to add up and individuality that will this moment as the chromosome of selecting.

4. described a kind of based on the cognitive radio approaches of improving genetic algorithm according to any claim among the claim 1-3, it is characterized in that the computing formula of said adaptive crossover probability is:

$P_c=\left\{\begin{array}{c}{P}_{c1}-\frac{(P_{c1}-P_{c2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{c1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.$

Wherein, P _cBe adaptive crossover probability, P _C1And P _C2Be respectively the upper and lower bound of crossover probability, f ' is the greater of the fitness value of two individuals that will intersect, f _MaxBe the maximum of population fitness value, f _AvgMean value for the population fitness value.

5. described a kind of based on the cognitive radio approaches of improving genetic algorithm according to any claim among the claim 1-3, it is characterized in that the computing formula of said adaptive variation probability is:

$P_m=\left\{\begin{array}{c}{P}_{m1}-\frac{(P_{m1}-P_{m2})\&times;(f^{\&prime;}-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},f^{\&prime;}\&GreaterEqual;f_{\mathrm{avg}}\\ P_{m1},f^{\&prime;}<f_{\mathrm{avg}}\end{array}\right.$

Wherein, P _mBe adaptive variation probability, P _M1And P _M2Be respectively the upper and lower bound of variation probability, f ' is the greater of the fitness value of two individuals that will intersect, f _MaxBe the maximum of population fitness value, f _AvgMean value for the population fitness value.

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