CN110769436A - Wireless communication anti-interference decision-making method based on mutation search artificial bee colony algorithm - Google Patents
Wireless communication anti-interference decision-making method based on mutation search artificial bee colony algorithm Download PDFInfo
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Abstract
The invention provides a wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm. The method comprises the following steps: firstly, selecting mutation search probability values with better performance; then obtaining the interference power of each channel; then inputting the interference information in the channel obtained by sensing, user requirements and constraint conditions into a multi-domain anti-interference decision engine together to obtain an anti-party model and an anti-interference efficiency function; and finally, searching an optimal communication parameter scheme through an artificial bee colony algorithm based on a mutation searching mechanism to obtain an optimal anti-interference effect value and a corresponding communication scheme, and finally setting communication parameters at a radio frequency end to send data. The method can overcome the defects of high decision-making scheme transmitting power and poor decision-making algorithm performance of the existing method under the condition of large multi-domain decision-making space, and under the condition, the system transmission rate is ensured to be maximized, meanwhile, the smaller transmitting power is realized, and meanwhile, the decision-making speed and the robustness are improved.
Description
Technical Field
The invention belongs to the technical field of cognitive communication countermeasure, and particularly relates to a wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm, which is a method for a wireless communication system to obtain system parameters for communication through multi-domain intelligent decision in an interference environment.
Background
With the increasing complexity of electromagnetic environment, a wireless communication system is extremely easy to be interfered by various kinds of interference, a multi-domain anti-interference decision has greater flexibility and gradually receives attention compared with the traditional single-domain anti-interference technology, and an intelligent decision algorithm is one of the cores of the technology, so that the finding of the intelligent decision algorithm with better effect has important significance.
At present, aiming at the research of multi-domain anti-interference intelligent decision algorithm, the adopted intelligent decision algorithm is mainly particle swarm algorithm, initial population optimization particle swarm algorithm, genetic algorithm based on simulated annealing mechanism, immune genetic algorithm and other methods, and the communication scheme obtained by the decision of the methods can maximize the transmission rate of the system, but the transmission power of the system is large, and the power resource is not saved enough. Meanwhile, the convergence rate and the algorithm robustness are to be improved.
Disclosure of Invention
The invention aims to provide a method which can overcome the defects of high decision-making scheme transmitting power and poor decision-making algorithm performance of the existing method under the condition of large multi-domain decision-making space, ensure the maximization of the system transmission rate, realize smaller transmitting power and improve the decision-making speed and robustness.
The technical scheme of the invention is as follows: a wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm comprises the following steps:
step A, interference data obtained by long-term sensing of an interference sensing system are utilized, the performance of a decision method under different interference environments and mutation search probabilities is tested, and mutation search probability values with better performance are selected;
b, when the system works, the interference sensing system senses to obtain interference information in the channel at the current moment and obtain the interference power of each channel;
step C, inputting the interference information in the channel obtained by sensing, user requirements and constraint conditions into a multi-domain anti-interference decision engine together to obtain an anti-party model and an anti-interference efficiency function;
step D, searching the optimal communication parameter scheme through an artificial bee colony algorithm based on a mutation searching mechanism to obtain the optimal anti-interference effect value and the corresponding communication scheme,
and E, setting communication parameters at the radio frequency end and sending data.
The invention provides a wireless communication multi-domain anti-interference decision method based on a mutation search mechanism artificial bee colony algorithm, aiming at the problems that the performance of a decision algorithm and the effect of a communication scheme are poor due to the fact that the multi-domain decision space is too large in wireless communication anti-interference decision, and a better decision scheme is obtained by introducing mutation search probability. The method is suitable for the wireless communication multi-domain anti-interference decision-making situation with a huge decision-making space, solves the problem that an optimal communication parameter configuration scheme cannot be found in a limited iteration process under the huge decision-making space, further reduces the transmitting power while maximizing the transmission rate, and has better adaptability of the algorithm to the environment and stronger robustness due to the variation search probability in the method.
The core technical content of the invention is that firstly, an optimal setting method of mutation search probability value is obtained through a period of test, then real-time interference information is obtained through an interference perception system, a decision model is established under certain user requirements and constraint conditions, and an optimal communication scheme is obtained through an artificial bee colony algorithm of a mutation search mechanism.
The invention comprises a test part for selecting mutation search probability, which mainly comprises the following contents: and setting mutation search probabilities of the decision algorithm to different values for testing under different interference sensing data through a plurality of groups of interference sensing data obtained by the interference sensing system for a long time, and obtaining the probability which can obtain the optimal decision scheme under different interference environments and has the highest decision speed as the mutation probability value of the decision algorithm.
The invention includes the anti-both-party model and anti-interference function part, whose main content is: the model part of the two counterpartners is mainly divided into an interference party model and a communication party model, the interference party model is expressed according to interference power in a channel, and the communication party model is established according to selectable transmitting frequency, transmitting power, a modulation mode and a coding mode. The wireless communication anti-interference decision mainly considers two decision targets of maximizing normalized transmission rate and minimizing transmitting power, and the anti-interference performance function is set by summing the two targets after giving different weights according to different user requirements.
The invention comprises an optimal communication scheme part obtained by an artificial bee colony algorithm based on a mutation search mechanism, which mainly comprises the following contents: setting decision algorithm parameters and mutation search probability, generating an initial strategy according to constraint conditions, calculating an initial population objective function value, then entering a honey collection mode, selecting a strategy with a higher objective function value by honey collection bees for mutation mechanism-based search, calculating an objective function value, and selecting a new strategy according to a greedy selection mechanism. Then calculating the following probability of the observation bees, entering an observation bee mode, selecting a certain strategy by the observation bees according to the probability for searching, wherein the searching mode is the same as that of the honey collection bees, calculating an objective function value, selecting a new strategy according to a greedy selection mechanism, then randomly generating a new strategy in the whole situation to replace the strategy exceeding the neighborhood searching limit times, judging whether the algorithm reaches the maximum iteration times, if so, obtaining a whole situation optimal communication scheme, setting communication parameters in a radio frequency range to send data, otherwise, returning to the honey collection bee mode.
The invention has the beneficial effects that: the invention provides a wireless communication multi-domain anti-interference decision method based on a mutation search artificial bee colony algorithm. The invention has the advantages that a mutation search mechanism is introduced into the bee colony algorithm as a decision algorithm, so that the transmitting power of an anti-interference decision obtained in a huge decision space is reduced on the basis of ensuring the maximum transmission rate, and meanwhile, the performance of the algorithm is improved.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
Fig. 1 is a flow for realizing a wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm.
Fig. 2100 sets of test curves for mutation search probability under simulated interference environment data.
Fig. 3 illustrates interference context awareness information at two different times.
Fig. 4 is a flow chart of a communication anti-interference decision algorithm based on a mutation search mechanism artificial bee colony algorithm.
FIG. 5 shows the convergence curves of the decision algorithm in two different interference environments.
Detailed Description
The embodiment provides a wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm, the implementation process of which is shown in fig. 1, and the method mainly comprises the following steps:
1. testing the performance of the decision method under different interference environments and mutation search probabilities by using interference data obtained by long-term perception of an interference perception system, and selecting mutation search probability values with better performance;
before the communication equipment is used, the equipment is placed in a working environment to be tested for a period of time, all interference environment data sensed by a sensing system in the time are recorded, the mutation search probability of a decision algorithm is changed to test a multi-domain anti-interference decision under the interference environment data, and the average iteration number and the average optimized anti-interference performance function value of the decision algorithm under each mutation search probability are recorded. Fig. 2 is a test curve of the mutation search probability under 100 sets of simulated interference environment data. And selecting the mutation search probability to be 0.2 under the simulated interference environment according to the test curve.
2. When the system works, the interference sensing system senses to obtain interference information in channels at the current moment and obtain the interference power of each channel;
when the system works, the sensing system is required to sense the interference power in each communication channel in real time and the interference power is used as the prior information of the decision engine. The interference perception information obtained at a certain time under the simulated interference environment is shown in fig. 3.
3. Inputting the interference information in the channel obtained by sensing, user requirements and constraint conditions into a multi-domain anti-interference decision engine together to obtain an anti-party model and an anti-interference efficiency function;
the two-party model of the countermeasure is divided into a communication party model and an interference party model, and the communication party system model is as follows:
(1) the system has K communication channels with non-overlapping frequencies, as shown in formula (1), and only one channel f is occupied by each communication of the systemiWherein i is 0, 1.
F＝{f0,f1,...,fK-1} (1)
(2) The system has M1The modulation mode is selectable and is represented as the following set:
(3) the system has M2The LDPC coding rate is selectable and is represented as the following set:
(4) the system has N transmit power alternatives, represented as the set:
PR＝{PR0,PR1,...,PR(N-1)} (4)
(5) different combinations of modulation schemes and coding rates of the system result in different transmission rates, and therefore the system has M-M1M2A transmission rate, expressed as a set of:
R＝{R0,R1,...,RM-1} (5)
after determining the decision variables of each domain of the system, the decision space of the decision engine can be expressed as:
W＝F×R×PR(6)
the number of solutions in the decision engine solution space is (KgMgN), and the optimal communication parameter configuration required to be adopted by the optimization algorithm in the current time slot interference environment is searched, which is expressed as:
the interferer system model is as follows: assuming that interference is present in all communication channels and the interference power in each channel is random, it can be expressed as:
PJ＝{PJ0,PJ1,...,PJ(K-1)} (8)
where K is the number of channels, PJiIs the interference power in the ith channel. The SINR at the receiving end is as follows:
wherein P isRjIs the transmission power, P, of the transmitterJiThe attenuation factor of the interference power at the receiving end is α, and is equal to or greater than 0 and equal to or less than α and equal to or less than 1.
After the models of the two counterpartners are established, a normalized anti-interference efficiency function is established according to the user requirements. In order to give consideration to two decision objectives of minimizing transmission power and maximizing transmission rate, the weights of the two decision objectives are obtained according to the user requirements, two decision objective functions are defined by adopting a normalization mode, and the normalization transmission rate is as follows (10):
where C is the code rate and M is the modulation order. Normalized transmit power is as in equation (11):
wherein P isRIs the transmit power. Converting the two targets into a normalized interference performance function by means of weight distribution, as shown in formula (12):
4. and searching an optimal communication parameter scheme through an artificial bee colony algorithm based on a mutation searching mechanism to obtain an optimal anti-interference effect value and a corresponding communication scheme, and finally setting communication parameters at a radio frequency end to send data.
An algorithm flow for performing a wireless communication multi-domain joint anti-interference decision by using an artificial bee colony algorithm based on a mutation search mechanism is shown in fig. 4, and the specific flow is as follows:
firstly, initializing algorithm parameters, and setting initial honey source number NP, domain search time limit, maximum iteration time maxcycle and mutation search probability cr.
Then generating an initial honey source, and representing the communication scheme by adopting a binary coding mode as follows:
wherein, K is the number of communication channels, M is the number of modulation and coding mode combinations of the system, i.e. the system can realize M kinds of normalized transmission rates, the system has N-level adjustable power, and the code length g of one individual is as follows:
g＝log2(KMN) (14)
and then entering a honey bee picking mode, and searching a new strategy by the honey bees according to the mutation searching probability. The mutation mechanism-based search formula is as follows:
wherein,
for individual X before searchingiAnd (c) at the j-th position, rand being a random number between 0 and 1,
for new individuals X obtained after searchkIs encoded at the j-th position of (1). And (3) after obtaining the new strategy according to the formula (15), calculating the anti-interference efficiency values of the new strategy and the strategy before searching, selecting the strategy with better anti-interference efficiency according to a greedy selection mechanism to continue searching, and ending the honey bee picking mode. The greedy selection mechanism can be represented by the probability distribution of the new honey source new _ X as follows:
and then entering an observation bee mode, selecting a strategy by the observation bee according to the probability proportional to the strategy anti-interference effect value, searching based on a mutation mechanism, calculating the anti-interference effect value according to a formula (15), converting the observation bee into a honey collection bee according to a greedy selection mechanism of a formula (17), replacing the original strategy, and ending the honey collection bee mode. The observation bee following probability is as follows:
wherein f (i) is the fitness value of the ith honey source, and N is the number of the honey sources when the observation bees are selected, and the value is equal to that of the bee-collecting bees. After the bee collection and observation modes are finished, a new strategy is randomly generated in the whole situation to replace the strategy exceeding the neighborhood search limit time limit, and finally whether the algorithm reaches the maximum iteration time maxcycle is judged, if yes, the current optimal communication parameter strategy is output, and if not, the bee collection mode is returned to continue to optimize.
In order to evaluate the effectiveness of the invention, the text is compared with a wireless communication multi-domain anti-interference decision algorithm based on a traditional artificial bee colony algorithm, the convergence speed of the decision algorithm, the normalized anti-interference effect value during convergence and a communication parameter scheme obtained after decision are taken as evaluation indexes, and the simulation parameters of the test are shown in table 1:
TABLE 1 test simulation parameters
The convergence curves for both algorithms are shown in fig. 5. In fig. 5, the decision algorithm convergence curve of MBABC vs ABC (single run) in the interference environment 1 is shown on the left, and the decision algorithm convergence curve of MBABC vs ABC (single run) in another interference environment is shown on the right. The ordinate is the normalized objective function value and the abscissa is the number of iterations.
The result of fig. 5 shows that, in the interference environment 1, the normalized objective function value obtained by the algorithm of the present invention in convergence is higher, and the problem that the conventional swarm algorithm falls into local optimum can be solved. Although the iteration times of the traditional artificial bee colony algorithm are smaller, the algorithm of the invention obtains the optimal solution within 100 iterations, and compared with the obvious improvement of the optimizing capability, the iteration time difference of about 20 times can be accepted. Under the interference environment 2, the objective function value obtained by the convergence of the algorithm is still superior to that of the traditional artificial bee colony algorithm. After the interference environment changes, the iteration number of the traditional artificial bee colony algorithm is increased from 50 times to about 200 times, and the iteration number of the algorithm is still maintained to be about 100 times. Therefore, the wireless communication multi-domain anti-interference decision algorithm based on the mutation search mechanism provided by the invention is more stable.
The communication parameter scheme resulting from the two algorithm decisions is shown in table 2:
table 2 communication parameter configuration scheme comparison (ω) under different interference environments1＝1/2，ω2＝1/2)
The results in table 2 show that when two targets are given the same weight, the algorithm of the present invention selects a channel with less interference power, and thus only 19.6dBm of transmit power is needed to achieve effective data transmission, which saves transmit power compared to the conventional artificial bee colony algorithm.
Finally, it should be noted that the above examples are only intended to describe the technical solutions of the present invention and not to limit the technical methods, the present invention can be extended in application to other modifications, variations, applications and embodiments, and therefore all such modifications, variations, applications, embodiments are considered to be within the spirit and teaching scope of the present invention.
Claims (6)
1. A wireless communication anti-interference decision method based on a mutation search artificial bee colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
step A, interference data obtained by long-term sensing of an interference sensing system are utilized, the performance of a decision method under different interference environments and mutation search probabilities is tested, and mutation search probability values with better performance are selected;
b, when the system works, the interference sensing system senses to obtain interference information in the channel at the current moment and obtain the interference power of each channel;
step C, inputting the interference information in the channel obtained by sensing, user requirements and constraint conditions into a multi-domain anti-interference decision engine together to obtain an anti-party model and an anti-interference efficiency function;
step D, searching the optimal communication parameter scheme through an artificial bee colony algorithm based on a mutation searching mechanism to obtain the optimal anti-interference effect value and the corresponding communication scheme,
and E, setting communication parameters at the radio frequency end and sending data.
2. The wireless communication anti-interference decision method based on the mutation search artificial bee colony algorithm according to claim 1, characterized in that: in the step A:
before the communication equipment is used, the equipment is placed in a working environment to be tested for a period of time, all interference environment data sensed by a sensing system in the time are recorded, the mutation search probability of a decision algorithm is changed to test a multi-domain anti-interference decision under the interference environment data, and the average iteration number and the average optimized anti-interference performance function value of the decision algorithm under each mutation search probability are recorded.
3. The wireless communication anti-interference decision method based on the mutation search artificial bee colony algorithm according to claim 1, characterized in that: in the step B:
firstly, a sensing system is required to sense the interference power in each communication channel in real time to be used as the prior information of a decision engine.
4. The wireless communication anti-interference decision method based on the mutation search artificial bee colony algorithm according to claim 1, characterized in that: in the step C
The two-party model of the countermeasure is divided into a communication party model and an interference party model;
the communication party system model is as follows:
the communication system has K communication channels with non-overlapping frequencies, as shown in formula (1), and the communication system only occupies one channel f for each communicationiWherein i is 0,1,.., K-1;
F＝{f0,f1,...,fK-1} (1)
the communication system has M1The modulation mode is selectable and is represented as the following set:
the communication system has M2The LDPC coding rate is selectable and is represented as the following set:
the communication system has N transmit power alternatives, represented as the set:
PR＝{PR0,PR1,...,PR(N-1)} (4)
the communication system has M ═ M1M2A transmission rate, expressed as a set of:
R＝{R0,R1,...,RM-1} (5)
the decision space of the decision engine can be expressed as:
W＝F×R×PR(6)
the number of solutions in the decision engine solution space is (KgM gN), and the optimal communication parameter configuration required to be adopted by the optimization algorithm in the current time slot interference environment is searched, which is expressed as:
the disturber system model is as follows:
PJ＝{PJ0,PJ1,...,PJ(K-1)} (8)
where K is the number of channels, PJiIs the interference power in the ith channel. The SINR at the receiving end is as follows:
wherein P isRjIs the transmission power, P, of the transmitterJiThe attenuation factor of the interference power at a receiving end is α, wherein the attenuation factor is more than or equal to 0 and less than or equal to α and less than or equal to 1;
after models of the two confrontation parties are established, a normalized anti-interference efficiency function is established according to user requirements;
two decision objective functions are defined in a normalization mode, and the normalized transmission rate is as follows:
wherein C is the coding rate and M is the modulation order;
normalized transmit power is as in equation (11):
wherein P isRIs the transmit power;
converting the two targets into a normalized interference performance function by means of weight distribution, as shown in formula (12):
5. the wireless communication anti-interference decision method based on the mutation search artificial bee colony algorithm according to claim 1, characterized in that:
in the step D:
step D01, initializing algorithm parameters, and setting initial honey source number NP, domain search time limit, maximum iteration time maxcycle and mutation search probability cr;
step D02, generating an initial honey source, and representing the communication scheme by adopting a binary coding mode as follows:
wherein, K is the number of communication channels, M is the number of modulation and coding mode combinations of the system, i.e. the system can realize M kinds of normalized transmission rates, the system has N-level adjustable power, and the code length g of one individual is as follows:
g＝log2(KMN) (14)
step D03, entering a honey bee picking mode, and searching a new strategy by the honey bee picking according to the mutation searching probability; the mutation mechanism-based search formula is as follows:
wherein,
for individual X before searchingiAnd (c) at the j-th position, rand being a random number between 0 and 1,for new individuals X obtained after searchkThe code at the j-th position of (1);
step D04, calculating the anti-interference effect values of the new strategy and the strategy before searching;
d05, selecting a strategy with better anti-interference performance according to a greedy selection mechanism to continue searching, and ending the honey bee picking mode; the greedy selection mechanism is represented by the probability distribution of a new honey source new _ X as follows:
and D06, entering an observation bee mode, selecting a strategy by the observation bee according to the probability proportional to the strategy anti-interference effect value, searching based on a mutation mechanism, calculating the anti-interference effect value, converting the observation bee into a bee collecting mode, replacing the original strategy, and ending the bee collecting mode.
6. The wireless communication anti-interference decision method based on the mutation search artificial bee colony algorithm according to claim 5, characterized in that:
the observation bee following probability is as follows:
wherein f (i) is the fitness value of the ith honey source, and N is the number of the honey sources when the observation bees are selected, and the value is equal to that of the bee-collecting bees.
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| CN111786738A (en) * | 2020-07-01 | 2020-10-16 | 中国人民解放军陆军工程大学 | Anti-interference learning network structure based on long-term and short-term memory and learning method |
| CN111865474A (en) * | 2020-07-15 | 2020-10-30 | 中国人民解放军国防科技大学 | Wireless communication anti-interference decision method and system based on edge calculation |
| CN112926832A (en) * | 2021-01-27 | 2021-06-08 | 哈尔滨工程大学 | Interference decision method based on directional mutation search artificial bee colony algorithm |
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| CN111786738A (en) * | 2020-07-01 | 2020-10-16 | 中国人民解放军陆军工程大学 | Anti-interference learning network structure based on long-term and short-term memory and learning method |
| CN111865474A (en) * | 2020-07-15 | 2020-10-30 | 中国人民解放军国防科技大学 | Wireless communication anti-interference decision method and system based on edge calculation |
| CN111865474B (en) * | 2020-07-15 | 2022-09-06 | 中国人民解放军国防科技大学 | Wireless communication anti-interference decision method and system based on edge calculation |
| CN112926832A (en) * | 2021-01-27 | 2021-06-08 | 哈尔滨工程大学 | Interference decision method based on directional mutation search artificial bee colony algorithm |
| CN113110517A (en) * | 2021-05-24 | 2021-07-13 | 郑州大学 | Multi-robot collaborative search method based on biological elicitation in unknown environment |
| CN113313262A (en) * | 2021-06-21 | 2021-08-27 | 哈尔滨工程大学 | Intelligent anti-interference decision-making method based on quantum world cup competition mechanism |
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