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CN119814218B - Anti-interference decision method and system based on quantum tail monkey mechanism - Google Patents

Anti-interference decision method and system based on quantum tail monkey mechanism

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Publication number
CN119814218B
CN119814218B CN202411934157.1A CN202411934157A CN119814218B CN 119814218 B CN119814218 B CN 119814218B CN 202411934157 A CN202411934157 A CN 202411934157A CN 119814218 B CN119814218 B CN 119814218B
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quantum
monkey
capuchin
measurement position
daidi
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CN119814218A (en
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高洪元
陈仕祺
生雪莉
刘曼馨
张炎
张梦卓
刘凯龙
褚依帆
李世瑞
武兴宇
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Harbin Engineering University
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Harbin Engineering University
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an anti-interference decision method based on a quantum tail monkey mechanism, and relates to the technical field of anti-interference decision. The method comprises the technical key points of constructing an anti-interference decision model, setting an objective function and constraint conditions based on the anti-interference decision model, and carrying out optimization solution on the objective function by utilizing a quantum tail monkey mechanism to obtain the optimal combination of the channel, the coding mode, the modulation mode and the transmitting power. The quantum tail monkey mechanism effectively improves the problems that the original tail monkey searching mechanism is too slow in convergence speed and easy to fall into a local optimal solution due to too large search space when the high-dimensional discrete optimization problem is solved, breaks through the problem that the original tail monkey searching mechanism is difficult to process discrete optimization and continuous optimization and has obvious superiority in the aspects of convergence speed, convergence precision and searching global optimal solution.

Description

Anti-interference decision method and system based on quantum tail monkey mechanism
Technical Field
The invention relates to the technical field of anti-interference decision making, in particular to an anti-interference decision making method and system based on a quantum tail monkey mechanism.
Background
The anti-interference decision method plays an important role in various fields of society, and particularly in national security. The anti-interference decision is to obtain an electromagnetic interference environment by a communication receiving end and feed back the electromagnetic interference environment to a sending end, and make a decision according to different electromagnetic environments, so that an optimal anti-interference strategy is selected, and safe and reliable transmission of information is ensured while efficient and economical communication is realized. For both communication party and interference party, the anti-interference decision is the process of game playing by both parties, especially in battlefield, the interference party does not know what frequency will be used by the party to transmit signals, but most of interference signals need to be generated aiming at the frequency, and different frequencies will be interfered by different degrees due to the occurrence of different conditions such as adjacent channel interference, spectrum leakage and multipath effect, and the higher transmitting power, better coding mode and modulation mode need high cost support. Therefore, the communication party can prepare a plurality of frequencies in advance at the transmitting and receiving ends to perform communication, and can switch between the frequencies when interference is received. If one frequency is affected by interference, the frequency is converted into another frequency which is not affected by interference for transmission, but the interference condition in the actual environment is more complex, and in the worst case, all the prepared frequencies are affected, only the frequency with the least influence degree can be selected for transmission. Meanwhile, the anti-interference performance can be improved through adjustment of a coding mode, a modulation mode and transmitting power. If a series of conditions of economic cost need to be considered, such as information durable war or limited resources, are considered in the future, the economic cost can be controlled from three aspects of coding mode, modulation mode and transmitting power. Therefore, it is of great importance to devise an anti-interference decision method that can control economic cost through reasonable resource allocation decision in the face of meeting information transmission rate requirements, and can maintain economical and highly reliable signal transmission in severe cases where frequency is selectable and the receiving end may suffer multiple interference.
Through the search discovery of the prior literature, ran Yu et al propose a cognitive anti-interference intelligent decision technique research based on an improved artificial bee colony mechanism on journal of signal processing (2019,35 (2), 240-249), the method improves the traditional anti-interference method by using the improved mechanism while realizing the improvement of the artificial bee colony mechanism, the global optimizing searching capability and the convergence speed of the method are improved to a certain extent, and the average convergence frequency is less and the optimal solution probability is improved. The intelligent decision method of the mechanism comprises four aspects of a channel, a modulation mode, a transmitting power and an interference suppression mode, and uses the multi-channel simulation multi-frequency condition, but only one interference type exists in each channel, and the complexity is high, so that the intelligent decision method has room for further discussion and improvement. Li et al published "SATELLITE COMMUNICATION ANTI-jamming based on artificial bee colony blind source separation" in 6th International Conference on Communication (2021, pp. 240-244) and proposed a method for researching satellite communication anti-interference by using artificial bee colony optimization mechanism to study blind source separation, further solving the defects of the current satellite communication anti-interference use in terms of spectral efficiency and anti-interference performance based on spread spectrum and filtering technology, and the result shows that the method can realize data transmission under the condition of strong interference, but also only analyzes the single interference condition and has higher complexity, and further has room for discussion and improvement.
The existing document retrieval results show that most of the processing of the existing anti-interference decision method is single-frequency single-interference without considering economic cost, but the actual interference and game situation is more complicated. If a series of conditions such as durable information war, limited resources and the like need to be considered, the economic cost is also one of the problems which need to be considered.
Disclosure of Invention
In view of the above problems, the invention provides an anti-interference decision method and system based on a quantum tail monkey mechanism.
According to an aspect of the present invention, an anti-interference decision method based on a quantum tail monkey mechanism is provided, the method comprising:
Step one, an anti-interference decision model is built, wherein the anti-interference decision model is a combination of a channel, a coding mode, a modulation mode and transmitting power of a communication party;
Setting an objective function and constraint conditions based on the anti-interference decision model;
and thirdly, optimizing and solving an objective function by utilizing a quantum tail monkey mechanism to obtain the optimal combination of a channel, a coding mode, a modulation mode and transmitting power.
Further, in the second step, the objective function is:
F(Cn,Mf,Iq,Kl)＝w1fber+w2fs
Wherein, C n represents the nth communication party optional channel, M f represents the f communication party optional modulation mode, I q represents the q communication party optional transmitting power, K l represents the first communication party optional coding mode; representing the minimum normalized average error rate, E max is the maximum average error rate, E min is the minimum average error rate, and E represents the average error rate; Representing minimum normalized transmit power, I max being maximum transmit power, I min being minimum transmit power, w 1 and w 2 being user desired parameters, w 1+w2 =1;
The constraint conditions are as follows:
Where f R＝Rb×Kl×log2(Mf) represents an information transmission rate, R b is an uncoded original information transmission rate set according to an actual environment at the time of communication, Representing the minimum information transmission rate required according to the actual environment at the time of communication.
Further, the specific steps of the third step include:
Initializing a quantum position in a quantum tail monkey mechanism;
measuring initial quantum positions of all quantum tail monkeys, setting an fitness function with a penalty function, calculating fitness values of all initial quantum positions, and determining a local optimal measurement position and a global optimal measurement position;
thirdly, updating the corresponding quantum rotation angle and the measuring position of the quantum position by different types of quantum tail monkeys according to respective updating strategies;
step three, converting the updated measurement position into a communication strategy, calculating the fitness value of the updated measurement position of each quantum tail monkey, and updating the local optimal measurement position and the global optimal measurement position;
step three, judging whether the search mechanism reaches the maximum iteration times, if so, outputting a global optimal measurement position, otherwise, returning to the step three, and continuing iteration;
And step III, converting the obtained global optimal measurement position into a corresponding channel, coding mode, modulation mode and transmitting power according to the mapping rule of the step III, and obtaining the optimal anti-interference decision combination.
Further, the specific steps of the third step include assuming that there are H quantum monkeys recorded as a quantum monkey group, H is taken as an even number, the quantum position of each quantum monkey has an S dimension, where S is the largest dimension of the solution space, The representation is rounded upwards, N is the total number of selectable channels of the communication party, F is the total number of selectable modulation modes of the communication party, L is the total number of selectable coding modes of the communication party, Q is the total number of selectable transmitting power of the communication party, and the quantum position of the nth quantum tail monkey of the t th generation isWherein the method comprises the steps ofT is the number of iterations, h=1, 2,..h, s=1, 2,..s, & S, let t=1 at the first time and each dimension of the quantum position of H quantum pigtails set toThe quantum position set of the whole quantum tail monkey group in the t th generation is
Further, the specific steps of the third step comprise:
the measurement position of the nth generation of the h quantum tail monkey is obtained by measurement The measurement equation of the s-th dimension quantum position of the t-th generation h quantum rolling monkey is thatWherein the method comprises the steps ofIs a random number obeying uniform distribution between [0,1],Will beMapping into according to mapping rulesAnd substituting into an anti-interference decision model, wherein the mapping rule is that
AndRespectively the nth generation and the h quantum tail monkeyOptional channels of individual communication parties, the thThe seed communication party can select a modulation mode, the thIndividual communication parties can select transmit power andThe communication party selects a coding mode;
Setting a punishment function as follows Calculating the adaptability value of the nth generation of the h quantum rolling monkey with the penalty function as follows:
The measurement position of the h quantum tail monkey with the t generation being the optimal value of the non-fitness is recorded as the local optimal measurement position Marking the measurement position of all quantum tail monkeys with the optimal stop fitness value from the t generation as the global optimal measurement position
Further, the specific steps of the third step comprise:
Front of quantum tail monkey group according to functions of tail monkeys The quantum monkey is always taken as a quantum leading monkey and a quantum accompanying monkey for seeking food, and the quantum leading monkey is marked as a quantum leading monkey group, and the quantum position set of the t th generation quantum leading monkey group isRear part (S)Only the quantum tail monkey is always taken as a quantum following monkey, and the quantum tail monkey is marked as a quantum following monkey group, and then the quantum position set of the t-th generation quantum following monkey group is
For quantum-leader monkey groups, two selection probabilities ε 1 and ε 2 are set, and then random numbers obeying uniform distribution between [0,1] are generatedWherein the method comprises the steps ofEpsilon 1∈[0,1],ε2∈[0,1],ε1+ε2 epsilon [0,1] -, ifDefinition of the t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofAs life index functions, beta 0、β1 and beta 2 are life index function fixed parameters,For the s-th dimension of the locally optimal measurement position,For the s-th dimension of the globally optimal measurement location,S=1, 2,..s, ifDefinition of the t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofTo randomly select the s-th dimension of the locally optimal measurement position of the quantum-leader monkey labeled u among the other quantum-leader monkeys,S=1, 2,..s, ifDefinition of the t th generationThe measurement positions of random azimuth feeding to be performed by only quantum-led monkeys areDefinition t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofIs the t generationThe s-th dimension of the measurement position of random azimuth feeding to be carried out by the quantum-leader monkey is as follows Is a random number obeying uniform distribution between [0,1], delta is a random azimuth feeding parameter,S=1, 2,..s, S; definition of the t+1st generationThe evolution method of the s-th dimension quantum position of the quantum-leaded monkey is thatWherein the method comprises the steps ofFor random numbers obeying uniform distribution between [0,1], c 1 represents the probability of variation of qubits at a 0-rotation angle of the equivalent, and its value isA constant value between the two,S=1, 2, S, gives the t+1st generationThe quantum position of the quantum-leader monkey alone isWherein the method comprises the steps ofThe quantum position set of the updated t+1st generation quantum leading monkey group is as follows
For quantum following monkey group, define the t+1st generationThe s-th dimension quantum rotation angle of the quantum following monkey is onlyWherein the method comprises the steps ofIs the t generationOnly the s-th dimension quantum rotation angle of the quantum-leaded monkey,Is the t generationOnly the quantum follows the s-th dimension quantum rotation angle of the monkey,Is a random number obeying uniform distribution between [0,1],S=1, 2,..s. Definition t+1st generationThe evolution method of the s-th dimension quantum position of the quantum following monkey only comprises the following steps ofWherein the method comprises the steps ofFor random numbers obeying uniform distribution between [0,1], c 2 represents the probability of variation of qubits at a 0-rotation angle of the equivalent, and its value isA constant value between the two,S=1, 2, S, gives the t+1st generationThe quantum position of the quantum following monkey is onlyWherein the method comprises the steps ofThe quantum position set of the updated t+1st generation quantum following monkey group is
Thereby obtaining the quantum position set of the whole quantum tail monkey group in the t+1st generation after updating asThe measurement position is obtained by measuring the s-th dimension quantum position of the (t+1) -th generation (h) -th quantum tail monkeyThe measurement equation isThe measured position of the (t+1) th generation of the (h) th quantum tail monkey isWherein the method comprises the steps ofAnd (3) obtaining the measurement position of the t+1th generation of H quantum tail monkeys after measurement, wherein the measurement position is the random number obeying uniform distribution between [0,1 ].
Further, the third and fourth steps comprise measuring the quantum position of the (t+1) th generation of the (h) th quantum tail monkeyMapping into according to mapping rulesAnd substituting the adaptive value into an anti-interference decision model to calculate the fitness value with a punishment functionThe measurement position of the h quantum tail monkey with the t+1st generation as the optimal value of the non-fitness is recorded as the local optimal measurement positionThe measurement position from the t+1st generation of all quantum tail monkeys to the optimal value of the non-fitness is recorded as the global optimal measurement position
According to another aspect of the present invention, an anti-interference decision system based on a quantum tail monkey mechanism is provided, the system comprising:
the model construction module is configured to construct an anti-interference decision model, and the anti-interference decision model is a combination of a channel, a coding mode, a modulation mode and transmitting power of a communication party;
An objective function design module configured to set an objective function and a constraint condition based on the tamper resistant decision model;
and the optimal decision solving module is configured to perform optimal solving on the objective function by utilizing a quantum tail monkey mechanism to obtain an optimal combination of a channel, a coding mode, a modulation mode and transmitting power.
Further, the objective function in the objective function design module is:
F(Cn,Mf,Iq,Kl)＝w1fber+w2fs
Wherein, C n represents the nth communication party optional channel, M f represents the f communication party optional modulation mode, I q represents the q communication party optional transmitting power, K l represents the first communication party optional coding mode; representing the minimum normalized average error rate, E max is the maximum average error rate, E min is the minimum average error rate, and E represents the average error rate; Representing minimum normalized transmit power, I max being maximum transmit power, I min being minimum transmit power, w 1 and w 2 being user desired parameters, w 1+w2 =1;
The constraint condition is that f R is not less than
Where f R＝Rb×Kl×log2(Mf) represents an information transmission rate, R b is an uncoded original information transmission rate set according to an actual environment at the time of communication,Representing the minimum information transmission rate required according to the actual environment at the time of communication.
Further, the step of obtaining the optimal combination of the channel, the coding mode, the modulation mode and the transmitting power by utilizing the quantum rolling monkey mechanism to perform optimal solution on the objective function in the optimal decision solving module comprises the following steps:
Initializing a quantum position in a quantum tail monkey mechanism;
measuring initial quantum positions of all quantum tail monkeys, setting an fitness function with a penalty function, calculating fitness values of all initial quantum positions, and determining a local optimal measurement position and a global optimal measurement position;
thirdly, updating the corresponding quantum rotation angle and the measuring position of the quantum position by different types of quantum tail monkeys according to respective updating strategies;
step three, converting the updated measurement position into a communication strategy, calculating the fitness value of the updated measurement position of each quantum tail monkey, and updating the local optimal measurement position and the global optimal measurement position;
step three, judging whether the search mechanism reaches the maximum iteration times, if so, outputting a global optimal measurement position, otherwise, returning to the step three, and continuing iteration;
And step III, converting the obtained global optimal measurement position into a corresponding channel, coding mode, modulation mode and transmitting power according to the mapping rule of the step III, and obtaining the optimal anti-interference decision combination.
The beneficial technical effects of the invention are as follows:
Under the condition of meeting the information transmission rate requirement, the invention controls the economic cost through the decision of reasonable resource allocation, and finds an optimal anti-interference decision, namely the transmitting frequency and transmitting power under the severe conditions that the frequency is optional and the receiving end is possibly subjected to multiple interference, so as to ensure the signal transmission in an economic and highly reliable way. Meanwhile, in order to solve the problems of high complexity and large calculation amount, the complexity is reduced by combining the anti-interference decision and the intelligent optimization mechanism. Compared with the prior art, the invention has the advantages that:
1) The electromagnetic environment faced in the real environment is various, different frequencies can overlap various interference conditions due to different conditions such as adjacent channel interference, spectrum leakage, multipath effect and the like, and the interference power of each interference in each channel is different. For the two ends of the receiving and transmitting of the communication party, a plurality of frequencies can be set as the selection of the receiving and transmitting of the signals, and when the receiving and transmitting of the signals are interfered, the strategy is quickly adjusted to be converted into the frequency without interference or the frequency with smaller interference so as to ensure the transmission of the signals. The invention simulates various electromagnetic environments, namely, various interferences possibly exist in one frequency and the powers of the interferences are different, and under the condition of meeting the information transmission rate requirement, the economic cost is controlled through the decision of reasonable resource allocation, and an optimal anti-interference strategy is found through a quantum rolling monkey mechanism, so that signals are transmitted in an economic and highly reliable mode.
2) Compared with the original rolling monkey searching mechanism, the quantum rolling monkey mechanism disclosed by the invention effectively improves the problems that the original rolling monkey searching mechanism is too slow in convergence speed and easy to sink into a local optimal solution due to overlarge searching space when the high-dimensional discrete optimizing problem is solved, breaks through the problem that the original rolling monkey searching mechanism is difficult to process discrete optimizing and continuous optimizing and has the same time, and can be seen on simulation results that the quantum rolling monkey mechanism has obvious superiority in the aspects of convergence speed, convergence precision and searching for a global optimal solution compared with the original rolling monkey searching mechanism. Compared with some classical population intelligent optimization mechanisms such as an improved particle swarm mechanism, the quantum tail monkey mechanism improves the searching efficiency, and can find the global optimal solution by using fewer iteration times, and the simulation result shows that the quantum tail monkey mechanism is obviously superior to the improved particle swarm mechanism in convergence speed.
Drawings
The invention may be better understood by reference to the following description taken in conjunction with the accompanying drawings, which are included to provide a further illustration of the preferred embodiments of the invention and to explain the principles and advantages of the invention, together with the detailed description below.
Fig. 1 is a flowchart of an anti-interference decision method based on a quantum tail monkey mechanism according to an embodiment of the present invention.
Fig. 2 is another flowchart of an anti-interference decision method based on a quantum tail monkey mechanism according to an embodiment of the present invention.
FIG. 3 is a graph showing an example of convergence curves of the optimization methods for the cases where the user desires to be 0.6 and 0.4 according to the embodiment of the present invention.
FIG. 4 is a graph showing an example of convergence curves of the optimization methods for the cases where the user desires to be 0.5 and 0.5 according to the embodiment of the present invention.
FIG. 5 is a graph showing an example of convergence curves of the optimization methods for the cases where the user desires to be 0.4 and 0.6 according to the embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. It is apparent that the described embodiments or examples are only implementations or examples of a part of the invention, not all. All other embodiments or examples, which may be made by one of ordinary skill in the art without undue burden, are intended to be within the scope of the present invention based on the embodiments or examples herein.
The invention considers that under the condition of meeting the information transmission rate requirement, the economic cost is controlled through the decision of reasonable resource allocation, and the optimal anti-interference decision is found under the bad condition that the frequency is optional and the receiving end is possibly subjected to multiple interference, so that the signal transmission is ensured in an economic and highly reliable way, and the anti-interference decision method based on the quantum rolling monkey mechanism is designed. The quantum optimization theory is used, the new quantum rotation angle is utilized to combine the change degree of the quantum rotation angle with the life index function of the rolling monkey searching mechanism, the balance of global and local searching is realized, the defects that the existing rolling monkey searching mechanism is too slow in convergence speed and too large in searching space and is easy to fall into local optimal solution when the high-dimensional discrete optimization problem is solved are effectively overcome, and the problem that the existing rolling monkey searching mechanism cannot solve the simultaneous problems of discrete optimization and continuous optimization is overcome.
The embodiment of the invention provides an anti-interference decision method based on a quantum tail monkey mechanism, as shown in fig. 1-2, comprising the following steps:
Step one, an anti-interference decision model is built, wherein the anti-interference decision model is a combination of a channel, a coding mode, a modulation mode and transmitting power of a communication party;
Setting an objective function and constraint conditions based on the anti-interference decision model;
the third step, the objective function is optimized and solved by utilizing a quantum tail monkey mechanism, and the optimal combination of the channel, the coding mode, the modulation mode and the transmitting power is obtained, and the method specifically comprises the following steps:
Initializing a quantum position in a quantum tail monkey mechanism;
measuring initial quantum positions of all quantum tail monkeys, setting an fitness function with a penalty function, calculating fitness values of all initial quantum positions, and determining a local optimal measurement position and a global optimal measurement position;
thirdly, updating the corresponding quantum rotation angle and the measuring position of the quantum position by different types of quantum tail monkeys according to respective updating strategies;
step three, converting the updated measurement position into a communication strategy, calculating the fitness value of the updated measurement position of each quantum tail monkey, and updating the local optimal measurement position and the global optimal measurement position;
and step three, judging whether the search mechanism reaches the maximum iteration times, if so, outputting a global optimal measurement position, and otherwise, returning to the step three, and continuing iteration.
And step III, converting the obtained global optimal measurement position into a corresponding channel, coding mode, modulation mode and transmitting power according to the mapping rule of the step III, and obtaining the optimal anti-interference decision combination.
The method starts in step one. In the first step, an anti-interference decision model is constructed, wherein the anti-interference decision model is a combination of a channel, a coding mode, a modulation mode and transmitting power of a communication party.
According to the embodiment of the invention, as shown in fig. 2, an intelligent anti-interference decision model is established, and key parameters of a quantum tail monkey mechanism corresponding to communication are determined. Setting a communication party selectable channel vector to c= [ C 1,C2,…,CN ], wherein N is a total number of communication party selectable channels, C n is an nth communication party selectable channel selected by the decision, n=1, 2; the communication party selectable modulation mode vector is m= [ M 1,M2,…,MF ], wherein F is the total number of communication party selectable modulation modes, M f is the F-th communication party selectable modulation mode selected by the decision, f=1, 2,..and F, the communication party selectable modulation mode vector is k= [ K 1,K2,…,KL ], wherein L is the total number of communication party selectable coding modes, K l is the first communication party selectable coding mode selected by the decision, l=1, 2, machine, L, the communication party selectable transmission power vector is i= [ I 1,I2,…,IQ ], wherein Q is the total number of communication party selectable transmission powers, I q is the Q-th communication party selectable transmission power selected by the decision, q=1, 2, machine, Q, the interference party selectable interference signal vector is y= [ Y 1,Y2,…,YA ], wherein a is the total number of interference party selectable interference signals, Y a is the a-th interference signal, a=1, 2, a, the interference party selectable interference power vector is j= [ J 1,J2,…,JB ], wherein B is the total number of interference party selectable transmission power, and b=2.
The probability of various interference signals occurring in the nth channel isWherein the method comprises the steps ofThe probability of occurrence of the a-th interferer selectable interfering signal in the N-th communication selectable channel, n=1, 2. Then generating random numbers subject to uniform distribution between corresponding [0,1] for each interference signal in nth channelWherein the method comprises the steps ofTo generate corresponding random numbers obeying uniform distribution between [0,1] for an a-th interferer in an N-th communication-party selectable channel, n=1, 2. If it isLess thanThen Y a interference is present in the nth channel and not otherwise, and each interference will randomly select interference power J b for transmission.
The intelligent anti-interference decision selection is divided into four aspects, namely a channel vector C, a coding mode vector K, a modulation mode vector M and a transmitting power vector I, and different anti-interference strategies D o＝[Cn,Mf,Iq,Kl can be obtained by combining different channels, coding modes, modulation modes and transmitting powers, wherein o=1, 2. The method comprises the steps of encoding randomly generated information with an encoding code rate K l, modulating the encoded information with a modulation mode M f, transmitting signals with a transmitting power I q to a channel C n for transmission, demodulating and decoding the signals received by a receiving end and comparing the signals with original information to obtain an error rate E 1, respectively marking the error rates obtained by calculation under the same simulation condition for a plurality of times as E 2、E3、…、Em-1 and E m, and marking the average error rate obtained by using an anti-interference method selected by decision asTherefore, the accidental of too large or too small interference signals is reduced, and m is the number of times of calculation under the same simulation condition.
And then executing a second step, wherein in the second step, an objective function and constraint conditions are set based on the anti-interference decision model.
According to the embodiment of the invention, starting from the index of the communication system, in order to realize an economical and highly reliable transmission mode, the objective function is set by using the minimum normalized average error rate and the minimum normalized transmitting power. Setting an objective function as follows:
F(Cn,Mf,Iq,Kl)＝w1fber+w2fs
Wherein, the For the minimum normalized average bit error rate obtained using the decision-selected anti-interference method, E max is the maximum average bit error rate, E min is the minimum average bit error rate,For the minimum normalized transmit power obtained using the decision-selected anti-interference method, I max is the maximum transmit power, I min is the minimum transmit power, w 1 and w 2 are the user desired parameters, w 1+w2 = 1. Since the average bit error rate and the transmission power are different in size and unit, they are normalized to unify dimensions, thereby obtaining a reasonable value to evaluate the policy goodness. The user expected parameter refers to the importance degree of the user to allocate the average error rate and the transmitting power according to the own requirement.
In the face of meeting the information transmission rate requirement, the economic cost is controlled through a decision of reasonable resource allocation so as to be used under a series of conditions of limited resources or durable information war and the like needing to consider the economic cost, and therefore constraint conditions are set as follows:
Wherein f R＝Rb×Kl×log2(Mf) is the information transmission rate obtained after using the anti-interference method selected by the decision, R b is the uncoded original information transmission rate set according to the actual environment during communication, Is the minimum information transmission rate required according to the actual environment when in communication.
And then executing a step three, wherein in the step three, an objective function is optimized and solved by utilizing a quantum tail monkey mechanism, and the optimal combination of a channel, a coding mode, a modulation mode and transmitting power is obtained.
First, in step three, the quantum position in the quantum tail mechanism is initialized.
According to the embodiment of the invention, it is assumed that H quantum tail monkeys are recorded as quantum tail monkey groups, and H is taken as an even number. The quantum position of each quantum pigtail has an S dimension, where S is the largest dimension of the solution space, Representing an upward rounding. Each quantum tail monkey has a respective quantum position, and the quantum position of the h quantum tail monkey in the t th generation isWherein the method comprises the steps ofT is the number of iterations, T is the maximum number of iterations, h=1, 2,..h, s=1, 2,..s. Let t=1 at the first generation, each dimension of the quantum position of h quantum pigtails is set toThe quantum position set of the whole quantum tail monkey group in the t th generation is
And in the third step, measuring the initial quantum position of each quantum tail monkey, setting an adaptability function with penalty, calculating the adaptability values of all the initial positions, and determining the local optimal measurement position and the global optimal measurement position.
According to the embodiment of the invention, the measurement position of the nth generation h quantum tail monkey is obtained by measurementThe measurement equation of the s-th dimension quantum position of the t-th generation h quantum rolling monkey is thatWherein the method comprises the steps ofIs a random number obeying uniform distribution between [0,1],H=1, 2,..h, s=1, 2,..s. Will beMapping to anti-interference method according to mapping ruleAnd substituting into an anti-interference decision model, wherein the mapping rule is that AndRespectively the nth generation and the h quantum tail monkeyOptional channels of individual communication parties, the thThe seed communication party can select a modulation mode, the thIndividual communication parties can select transmit power andA communication party can select an encoding mode.
Setting a punishment function as followsWhere ρ is a penalty coefficient.
The adaptability value with punishment of the nth generation of the h quantum tail monkey is calculated as follows:
The measurement position of the h quantum tail monkey with the t generation being the optimal value of the non-fitness is recorded as the local optimal measurement position Marking the measurement position of all quantum tail monkeys with the optimal stop fitness value from the t generation as the global optimal measurement position
Then, in the third step, different types of quantum pigtails update the corresponding quantum rotation angles and measurement positions of quantum positions according to respective update strategies.
According to the embodiment of the invention, the front part of the quantum pigtail group is based on the function of the pigtailThe quantum monkey is always taken as a quantum leading monkey and a quantum accompanying monkey for seeking food, and the quantum leading monkey is marked as a quantum leading monkey group, and the quantum position set of the t th generation quantum leading monkey group isRear part (S)Only the quantum tail monkey is always taken as a quantum following monkey, and the quantum tail monkey is marked as a quantum following monkey group, and then the quantum position set of the t-th generation quantum following monkey group is
For quantum-leaded monkey groups, two selection probabilities ε 1 and ε 2 are set, and then random numbers subject to uniform distribution between [0,1] are generatedWherein the method comprises the steps ofEpsilon 1∈[0,1],ε2∈[0,1],ε1+ε2 epsilon [0,1]. If it isDefinition of the t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofAs life index functions, beta 0、β1 and beta 2 are life index function fixed parameters,For the s-th dimension of the locally optimal measurement position,For the s-th dimension of the globally optimal measurement location,S=1, 2,..s, ifDefinition of the t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofTo randomly select the s-th dimension of the locally optimal measurement position of the quantum-leader monkey labeled u among the other quantum-leader monkeys,S=1, 2,..s, ifDefinition of the t th generationThe measurement positions of random azimuth feeding to be performed by only quantum-led monkeys areDefinition t+1st generationThe s-th dimension quantum rotation angle of the quantum-guided monkey is onlyWherein the method comprises the steps ofIs the t generationThe s-th dimension of the measurement position of random azimuth feeding to be carried out by the quantum-leader monkey is as follows Is a random number obeying uniform distribution between [0,1], delta is a random azimuth feeding parameter,S=1, 2,..s. Definition t+1st generationThe evolution method of the s-th dimension quantum position of the quantum-leaded monkey is thatWherein the method comprises the steps ofFor random numbers obeying uniform distribution between [0,1], c 1 represents the probability of variation of qubits at a 0-rotation angle of the equivalent, and its value isA constant value between the two,S=1, 2,..s. Obtain the t+1st generationThe quantum position of the quantum-leader monkey alone isWherein the method comprises the steps ofThe quantum position set of the updated t+1st generation quantum leading monkey group is as follows
For the quantum following monkey group, define the t+1st generationThe s-th dimension quantum rotation angle of the quantum following monkey is onlyWherein the method comprises the steps ofIs the t generationOnly the s-th dimension quantum rotation angle of the quantum-leaded monkey,Is the t generationOnly the quantum follows the s-th dimension quantum rotation angle of the monkey,Is a random number obeying uniform distribution between [0,1],S=1, 2,..s. Definition t+1st generationThe evolution method of the s-th dimension quantum position of the quantum following monkey only comprises the following steps ofWherein the method comprises the steps ofFor random numbers obeying uniform distribution between [0,1], c 2 represents the probability of variation of qubits at a 0-rotation angle of the equivalent, and its value isA constant value between the two,S=1, 2,..s. Obtain the t+1st generationThe quantum position of the quantum following monkey is onlyWherein the method comprises the steps ofThe quantum position set of the updated t+1st generation quantum following monkey group is
Thereby obtaining the quantum position set of the whole quantum tail monkey group in the t+1st generation after updating asThe measurement position is obtained by measuring the s-th dimension quantum position of the (t+1) -th generation (h) -th quantum tail monkeyThe measurement equation isThe measured position of the (t+1) th generation of the (h) th quantum tail monkey isWherein the method comprises the steps ofIs a random number obeying uniform distribution between [0,1], h=1, 2,.. s. measurement results in the measurement position of the t+1st generation of H quantum tail monkeys.
And in the third and fourth steps, converting the updated measurement position into a communication strategy, calculating the fitness value of the updated measurement position of each quantum tail monkey, and updating the local optimal measurement position and the global optimal measurement position.
According to the embodiment of the invention, after the quantum position of the (t+1) th generation of the (h) th quantum tail monkey is measured, the (h) th quantum tail monkey isMapping to anti-interference method according to mapping ruleSubstituting the adaptive value into an anti-interference decision model to calculate the adaptability value with punishmentWhere h=1, 2,..h. The measurement position of the h quantum tail monkey with the t+1st generation as the optimal value of the non-fitness is recorded as the local optimal measurement positionThe measurement position from the t+1st generation of all quantum tail monkeys to the optimal value of the non-fitness is recorded as the global optimal measurement position
And then in the third step, judging whether the search mechanism reaches the maximum iteration times, if so, outputting a global optimal measurement position, otherwise, returning t=t+1 to the third step, and continuing iteration.
Then, in the third and sixth steps, the obtained global optimal measurement position is converted into a corresponding channel, a coding mode, a modulation mode and transmitting power according to the mapping rule of the third and second steps, and the optimal anti-interference decision combination is obtained.
Further experiments prove the technical effect of the invention.
The anti-interference decision method of the quantum monkey with the rolling mill mechanism is abbreviated as QCapSA, and the search mechanism for comparison is the anti-interference decision method of the monkey with the rolling mill mechanism and the anti-interference decision method of the improved particle swarm mechanism, which are abbreviated as CapSA and IPO-PSO respectively.
In order to comprehensively compare the performances of the three methods, the same initialization is performed on QCAPSA, capSA and IPO-PSO, and the same communication system parameters are set: R b＝107 bits/s, n= 4;F =4, BPSK, QPSK, 16QAM and 64QAM respectively, l=4, 1 code, i.e. no code, respectively Coding(s),Coding and encodingCoding, Q=64, the transmitting power range is-11 dB to 20.5dB, the resolution is 0.5dB, E max＝0.5,Emin＝10-6;Imax＝20.5,Imin = -11, B=10, the interference power range of the interfering party is 21dB to 30dB, the resolution is 1dB, A=3, 50% of partial band interference, 10% of comb interference and 10% of single-tone sweep interference are respectively, and the occurrence probability of each interference in each channel is all that
S=12 is set in QCapSA, the penalty function p=10 -7,Epsilon 1＝0.8,ε2＝0.1,β0＝2,β1＝21,β2 =2, δ=0.5. After observing the quantum tail monkey, the first 2-dimensional corresponding channel is ' 00' representing the 1 st channel, 01 ' representing the 2 nd channel, 10 ' representing the 3 rd channel and 11 ' representing the 4 th channel, 3 rd and 4 th dimensions represent the modulation mode, 00' representing BPSK, 01 ' representing QPSK, 10 ' representing 16QAM and 11 ' representing 64QAM, 5 th to 10 th dimensions correspond to the transmitting power, 000000 ' representing-11 dB, 000001 ' representing-10.5 dB, and the like until ' 111111 ' represents 20.5dB, the corresponding resolution is 0.5dB, the last 2 dimensions represent the coding mode, 00' representing 1 code, 01 ' representing 1 codeCode, "10" stands forCode and "11" representEncoding. The population scale and the maximum iteration number of the three mechanisms are the same, namely H=20 and T=1000, and the fitness curve is drawn by taking the fitness average value of 500 runs. Other relevant parameter settings of CapSA are shown in Malik Braik et al, neural Computing and Applications (2021,Volume 33,pages 2515-2547) and other relevant parameter settings of "Anovel meta-heuristic search algorithm for solving optimization problems:capuchin search algorithm".IPO-PSO are shown in Hui Xianyang et al, communication technology (2015,Volume 48,No.7), communication anti-interference decision engine based on initial population optimization particle swarm mechanism.
Fig. 3-5 show examples of convergence curves of the optimization methods when different user desired parameters, from which it can be seen that QCapSA has obvious advantages over CapSA in terms of convergence speed, convergence accuracy and searching for a globally optimal solution, and although the IPO-PSO can find a globally optimal solution in a larger number of simulation runs, it is obvious that it has a much weaker disadvantage than QCapSA in terms of convergence speed.
Another embodiment of the present invention provides an anti-interference decision system based on a quantum tail monkey mechanism, the system comprising:
the model construction module is configured to construct an anti-interference decision model, and the anti-interference decision model is a combination of a channel, a coding mode, a modulation mode and transmitting power of a communication party;
An objective function design module configured to set an objective function and a constraint condition based on the tamper resistant decision model;
and the optimal decision solving module is configured to perform optimal solving on the objective function by utilizing a quantum tail monkey mechanism to obtain an optimal combination of a channel, a coding mode, a modulation mode and transmitting power.
In this embodiment, preferably, the objective function in the objective function design module is:
F(Cn,Mf,Iq,Kl)＝w1fber+w2fs
Wherein, C n represents the nth communication party optional channel, M f represents the f communication party optional modulation mode, I q represents the q communication party optional transmitting power, K l represents the first communication party optional coding mode; representing the minimum normalized average error rate, E max is the maximum average error rate, E min is the minimum average error rate, and E represents the average error rate; Representing minimum normalized transmit power, I max being maximum transmit power, I min being minimum transmit power, w 1 and w 2 being user desired parameters, w 1+w2 =1;
The constraint conditions are as follows:
Where f R＝Rb×Kl×log2(Mf) represents an information transmission rate, R b is an uncoded original information transmission rate set according to an actual environment at the time of communication, Representing the minimum information transmission rate required according to the actual environment at the time of communication.
In this embodiment, preferably, the step of obtaining an optimal combination of a channel, a coding mode, a modulation mode and a transmitting power in the optimal decision solving module by using a quantum tail monkey mechanism to perform optimal solution on an objective function includes:
Initializing a quantum position in a quantum tail monkey mechanism;
measuring initial quantum positions of all quantum tail monkeys, setting an fitness function with a penalty function, calculating fitness values of all initial quantum positions, and determining a local optimal measurement position and a global optimal measurement position;
thirdly, updating the corresponding quantum rotation angle and the measuring position of the quantum position by different types of quantum tail monkeys according to respective updating strategies;
step three, converting the updated measurement position into a communication strategy, calculating the fitness value of the updated measurement position of each quantum tail monkey, and updating the local optimal measurement position and the global optimal measurement position;
step three, judging whether the search mechanism reaches the maximum iteration times, if so, outputting a global optimal measurement position, otherwise, returning to the step three, and continuing iteration;
And step III, converting the obtained global optimal measurement position into a corresponding channel, coding mode, modulation mode and transmitting power according to the mapping rule of the step III, and obtaining the optimal anti-interference decision combination.
The function of the anti-interference decision system based on the quantum tail monkey mechanism according to the embodiment of the present invention may be illustrated by the anti-interference decision method based on the quantum tail monkey mechanism, so that the system embodiment is not described in detail, and reference may be made to the above method embodiment, which is not described herein.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.

Claims (6)

1.一种基于量子卷尾猴机制的抗干扰决策方法，其特征在于，包括：1. An anti-interference decision-making method based on quantum capuchin mechanism, characterized by comprising: 步骤一、构建抗干扰决策模型，所述抗干扰决策模型为通信方的信道、编码方式、调制方式和发射功率的组合；Step 1: construct an anti-interference decision model, where the anti-interference decision model is a combination of the communication party's channel, coding mode, modulation mode and transmission power; 步骤二、基于所述抗干扰决策模型设置目标函数和约束条件；所述目标函数为：Step 2: Setting the objective function and constraints based on the anti-interference decision model; the objective function is: ； ; 其中，表示第个通信方可选信道；表示第种通信方可选调制方式；表示第个通信方可选发射功率；表示第种通信方可选编码方式；表示最小归一化平均误码率，为最大平均误码率，为最小平均误码率，表示平均误码率；表示最小归一化发射功率，为最大发射功率，为最小发射功率；和为用户期望参数，；in, Indicates Each communication party can select a channel; Indicates Optional modulation modes for communication parties; Indicates Each communicating party can select the transmission power; Indicates Optional encoding methods for communication parties; represents the minimum normalized mean bit error rate, is the maximum average bit error rate, is the minimum average bit error rate, represents the average bit error rate; represents the minimum normalized transmit power, is the maximum transmit power, is the minimum transmit power; and is the user's expected parameter, ; 所述约束条件为：；The constraints are: ; 其中，表示信息传输速率，为通信时根据实际环境所设置的未编码的原始信息传输速率，表示通信时根据实际环境所需的最小信息传输速率；in, Indicates the information transmission rate, It is the original uncoded information transmission rate set according to the actual environment during communication. Indicates the minimum information transmission rate required for communication according to the actual environment; 步骤三、利用量子卷尾猴机制对目标函数进行优化求解，获取信道、编码方式、调制方式和发射功率的最优组合；包括：Step 3: Use the quantum capuchin mechanism to optimize and solve the objective function to obtain the optimal combination of channel, coding method, modulation method and transmission power; including: 步骤三一、初始化量子卷尾猴机制中的量子位置；Step 31: Initialize the quantum position in the quantum capuchin mechanism; 步骤三二、对各量子卷尾猴的初始量子位置进行测量，设置带有惩罚函数的适应度函数并计算所有初始量子位置的适应度值，确定局部最优测量位置和全局最优测量位置；Step 32: Measure the initial quantum position of each quantum capuchin, set a fitness function with a penalty function and calculate the fitness values of all initial quantum positions, and determine the local optimal measurement position and the global optimal measurement position; 步骤三三、不同类型的量子卷尾猴根据各自更新策略来更新相应的量子旋转角和量子位置的测量位置；Step 3. Different types of quantum capuchins update the corresponding measured positions of quantum rotation angles and quantum positions according to their respective update strategies. 步骤三四、将更新后的测量位置转化为通信策略，计算每只量子卷尾猴更新后测量位置的适应度值，更新局部最优测量位置和全局最优测量位置；Step 34: Convert the updated measurement position into a communication strategy, calculate the fitness value of each quantum capuchin's updated measurement position, and update the local optimal measurement position and the global optimal measurement position; 步骤三五、判断搜索机制是否达到最大迭代次数，若是则输出全局最优测量位置；否则返回步骤三三继续迭代；Step 35: determine whether the search mechanism has reached the maximum number of iterations, if so, output the global optimal measurement position; otherwise, return to step 33 to continue iterating; 步骤三六、将得到的全局最优测量位置根据步骤三二的映射规则转化为相应的信道、编码方式、调制方式和发射功率，获得最优抗干扰决策组合。Step 36: Convert the obtained global optimal measurement position into the corresponding channel, coding mode, modulation mode and transmission power according to the mapping rule of step 32 to obtain the optimal anti-interference decision combination. 2.根据权利要求1所述的一种基于量子卷尾猴机制的抗干扰决策方法，其特征在于，步骤三一的具体步骤包括：假设存在只量子卷尾猴记为量子卷尾猴群，取为偶数；每一只量子卷尾猴的量子位置都具有维，其中为解空间的最大维数，代表向上取整，为通信方可选信道总数，为通信方可选调制方式总数，为通信方可选编码方式总数，为通信方可选发射功率总数；第代第只量子卷尾猴的量子位置为，其中为迭代次数，初代时令只量子卷尾猴的量子位置的各维均设为，则第代整个量子卷尾猴群的量子位置集合为。2. According to the anti-interference decision-making method based on the quantum capuchin mechanism of claim 1, it is characterized in that the specific steps of step 31 include: assuming that there is Quantum capuchins are recorded as quantum capuchin groups. is an even number; the quantum position of each quantum capuchin has Dimension, where is the maximum dimension of the solution space, represents rounding up, is the total number of channels available to the communication parties, is the total number of modulation modes that can be selected by the communication parties, is the total number of encoding methods that can be selected by the communication parties, is the total number of optional transmission powers for the communication parties; Daidi The quantum position of the quantum capuchin is ,in is the number of iterations, First season The dimensions of the quantum position of a quantum capuchin are set to , then The quantum position set of the entire quantum capuchin group is . 3.根据权利要求2所述的一种基于量子卷尾猴机制的抗干扰决策方法，其特征在于，步骤三二的具体步骤包括：3. According to the anti-interference decision-making method based on the quantum capuchin mechanism of claim 2, it is characterized in that the specific steps of step 32 include: 通过测量得到第代第只量子卷尾猴的测量位置为，第代第只量子卷尾猴第维量子位置的测量方程为，其中为[0,1]之间服从均匀分布的随机数，；将根据映射规则映射为并代入抗干扰决策模型，其中映射规则为， 和分别为第代第只量子卷尾猴所选择的第个通信方可选信道、第种通信方可选调制方式、第个通信方可选发射功率和第种通信方可选编码方式；By measuring the Daidi The measured position of the quantum capuchin monkey is , No. Daidi Quantum Capuchin Monkey The measurement equation of the dimensional quantum position is ,in is a random number that follows a uniform distribution between [0,1]. ;Will According to the mapping rules, it is mapped to And substitute it into the anti-interference decision model, where the mapping rule is , and Respectively Daidi The first quantum capuchin monkey The communication party can select the channel, The communication parties can select the modulation mode, Each communicating party can select the transmission power and Optional encoding methods for communication parties; 根据约束条件设置惩罚函数为，其中为惩罚系数；计算第代第只量子卷尾猴的带有惩罚函数的适应度值为：According to the constraints, the penalty function is set as ,in is the penalty coefficient; calculate the Daidi The fitness value of a quantum capuchin with a penalty function is: ； ; 将第只量子卷尾猴截至第代为止适应度值最优的测量位置记为局部最优测量位置，将全部量子卷尾猴截至第代为止适应度值最优的测量位置记为全局最优测量位置。The first Quantum capuchin monkeys The measurement position with the best fitness value so far is recorded as the local optimal measurement position , all quantum capuchins up to the The measurement position with the best fitness value so far is recorded as the global optimal measurement position. . 4.根据权利要求3所述的一种基于量子卷尾猴机制的抗干扰决策方法，其特征在于，步骤三三的具体步骤包括：4. According to the anti-interference decision-making method based on the quantum capuchin mechanism of claim 3, it is characterized in that the specific steps of step three three include: 根据卷尾猴的职能将量子卷尾猴群的前只量子卷尾猴始终作为外出寻找食物的量子领导猴及量子伴生卷尾猴，记为量子领导猴群，则第代量子领导猴群的量子位置集合为；后只量子卷尾猴始终作为量子跟随猴，记为量子跟随猴群，则第代量子跟随猴群的量子位置集合为；According to the functions of the capuchins, the front of the quantum capuchin group The quantum capuchin monkeys always serve as the quantum leader monkeys and quantum companion capuchin monkeys that go out to find food, recorded as the quantum leader monkey group, then The quantum position set of the generation quantum leader monkey group is ;back The quantum capuchin monkeys always act as quantum follower monkeys, recorded as quantum follower monkey group, then The quantum position set of the generation quantum following the monkey group is ; 对于量子领导猴群，设置两个选择概率和，随后产生[0,1]之间服从均匀分布的随机数，其中；若，则定义第代第只量子领导猴的第维量子旋转角为，其中为寿命指数函数，和为寿命指数函数固定参数，为局部最优测量位置的第维，为全局最优测量位置的第维，；若，则定义第代第只量子领导猴的第维量子旋转角为，其中为在其它量子领导猴中随机选择标号为的量子领导猴的局部最优测量位置的第维，；若，则定义第代第只量子领导猴将要进行的随机方位寻食的测量位置为，定义第代第只量子领导猴的第维量子旋转角为，其中为第代第只量子领导猴将要进行的随机方位寻食的测量位置的第维，其测量方程为，为[0,1]之间服从均匀分布的随机数，为随机方位寻食参数，；定义第代第只量子领导猴的第维量子位置的演化方法为，其中为[0,1]之间服从均匀分布的随机数，代表当量子旋转角为0时量子位产生变异的概率，其值为之间的常量，；得到第代第只量子领导猴的量子位置为，其中；则更新后第代量子领导猴群的量子位置集合为；For the quantum leader monkey group, set two selection probabilities and , and then generate a random number between [0,1] that follows a uniform distribution ,in ;like , then define Daidi The first quantum leader monkey The quantum rotation angle is ,in is the life exponential function, and is a fixed parameter of the life exponential function, is the local optimal measurement position. dimension, is the global optimal measurement position. dimension, ;like , then define Daidi The first quantum leader monkey The quantum rotation angle is ,in To randomly select a leader monkey with the number The local optimal measurement position of the quantum leader monkey dimension, ;like , then define Daidi The measured position of the random orientation foraging that the quantum leader monkey will perform is , define Daidi The first quantum leader monkey The quantum rotation angle is ,in For the Daidi The first measurement position of the random orientation foraging that the quantum leader monkey will conduct dimension, its measurement equation is , is a random number that follows a uniform distribution between [0,1]. is the random orientation foraging parameter, ; Definition Daidi The first quantum leader monkey The evolution method of the dimensional quantum position is ,in is a random number that follows a uniform distribution between [0,1]. Represents the probability of a quantum bit mutating when the quantum rotation angle is 0, and its value is The constant between ; get the first Daidi The quantum position of the quantum leader monkey is ,in ; then after updating The quantum position set of the generation quantum leader monkey group is ; 对于量子跟随猴群，定义第代第只量子跟随猴的第维量子旋转角为，其中为第代第只量子领导猴的第维量子旋转角，为第代第只量子跟随猴的第维量子旋转角，为[0,1]之间服从均匀分布的随机数，；定义第代第只量子跟随猴的第维量子位置的演化方法为，其中为[0,1]之间服从均匀分布的随机数，代表当量子旋转角为0时量子位产生变异的概率，其值为之间的常量，；得到第代第只量子跟随猴的量子位置为，其中；则更新后第代量子跟随猴群的量子位置集合为；For the quantum monkey group, define Daidi The first quantum monkey The quantum rotation angle is ,in For the Daidi The first quantum leader monkey dimensional quantum rotation angle, For the Daidi The first quantum monkey dimensional quantum rotation angle, is a random number that follows a uniform distribution between [0,1]. ; Definition Daidi The first quantum monkey The evolution method of the dimensional quantum position is ,in is a random number that follows a uniform distribution between [0,1]. Represents the probability of a quantum bit mutating when the quantum rotation angle is 0, and its value is The constant between ; get the first Daidi The quantum position of the monkey following is ,in ; then after updating The quantum position set of the generation quantum following the monkey group is ; 进而得到更新后第代整个量子卷尾猴群的量子位置集合为；通过对第代第只量子卷尾猴的第维量子位置进行测量得到测量位置，其测量方程为，则第代第只量子卷尾猴经过测量后的测量位置为，其中为[0,1]之间服从均匀分布的随机数，测量后得到只量子卷尾猴的第代的测量位置。Then get the updated The quantum position set of the entire quantum capuchin group is ; Through the Daidi The first quantum capuchin monkey The measured position is obtained by measuring the quantum position , and its measurement equation is , then Daidi The measured position of the quantum capuchin monkey after measurement is ,in is a random number that follows a uniform distribution between [0,1]. After measurement, we get The first quantum capuchin monkey The measurement location of the generation. 5.根据权利要求4所述的一种基于量子卷尾猴机制的抗干扰决策方法，其特征在于，步骤三四的具体步骤包括：第代第只量子卷尾猴的量子位置经过测量后，将根据映射规则映射为并代入抗干扰决策模型中，计算带有惩罚函数的适应度值；将第只量子卷尾猴截至第代为止适应度值最优的测量位置记为局部最优测量位置，将全部量子卷尾猴截至第代为止适应度值最优的测量位置记为全局最优测量位置。5. According to claim 4, the anti-interference decision-making method based on the quantum capuchin mechanism is characterized in that the specific steps of steps three and four include: Daidi After the quantum position of a quantum capuchin is measured, According to the mapping rules, it is mapped to And substitute it into the anti-interference decision model to calculate the fitness value with penalty function ; Quantum capuchin monkeys The measurement position with the best fitness value so far is recorded as the local optimal measurement position , all quantum capuchins up to the The measurement position with the best fitness value so far is recorded as the global optimal measurement position. . 6.一种基于量子卷尾猴机制的抗干扰决策系统，其特征在于，包括：6. An anti-interference decision-making system based on quantum capuchin mechanism, characterized by comprising: 模型构建模块，其配置成构建抗干扰决策模型，所述抗干扰决策模型为通信方的信道、编码方式、调制方式和发射功率的组合；A model building module, configured to build an anti-interference decision model, wherein the anti-interference decision model is a combination of a channel, a coding method, a modulation method, and a transmission power of a communication party; 目标函数设计模块，其配置成基于所述抗干扰决策模型设置目标函数和约束条件；所述目标函数设计模块中所述目标函数为：An objective function design module is configured to set an objective function and constraint conditions based on the anti-interference decision model; the objective function in the objective function design module is: ； ; 其中，表示第个通信方可选信道；表示第种通信方可选调制方式；表示第个通信方可选发射功率；表示第种通信方可选编码方式；表示最小归一化平均误码率，为最大平均误码率，为最小平均误码率，表示平均误码率；in, Indicates Each communication party can select a channel; Indicates Optional modulation modes for communication parties; Indicates Each communicating party can select the transmission power; Indicates Optional encoding methods for communication parties; represents the minimum normalized average bit error rate, is the maximum average bit error rate, is the minimum average bit error rate, represents the average bit error rate; 表示最小归一化发射功率，为最大发射功率，为最小发射功率；和为用户期望参数，； represents the minimum normalized transmit power, is the maximum transmit power, is the minimum transmit power; and is the user's expected parameter, ; 所述约束条件为：；The constraints are: ; 其中，表示信息传输速率，为通信时根据实际环境所设置的未编码的原始信息传输速率，表示通信时根据实际环境所需的最小信息传输速率；in, Indicates the information transmission rate, It is the original uncoded information transmission rate set according to the actual environment during communication. Indicates the minimum information transmission rate required for communication according to the actual environment; 最优决策求解模块，其配置成利用量子卷尾猴机制对目标函数进行优化求解，获取信道、编码方式、调制方式和发射功率的最优组合，包括：The optimal decision-solving module is configured to optimize and solve the objective function using the quantum capuchin mechanism to obtain the optimal combination of channel, coding mode, modulation mode and transmission power, including: 步骤三一、初始化量子卷尾猴机制中的量子位置；Step 31: Initialize the quantum position in the quantum capuchin mechanism; 步骤三二、对各量子卷尾猴的初始量子位置进行测量，设置带有惩罚函数的适应度函数并计算所有初始量子位置的适应度值，确定局部最优测量位置和全局最优测量位置；Step 32: Measure the initial quantum position of each quantum capuchin, set a fitness function with a penalty function and calculate the fitness values of all initial quantum positions, and determine the local optimal measurement position and the global optimal measurement position; 步骤三三、不同类型的量子卷尾猴根据各自更新策略来更新相应的量子旋转角和量子位置的测量位置；Step 3. Different types of quantum capuchins update the corresponding measured positions of quantum rotation angles and quantum positions according to their respective update strategies. 步骤三四、将更新后的测量位置转化为通信策略，计算每只量子卷尾猴更新后测量位置的适应度值，更新局部最优测量位置和全局最优测量位置；Step 34: Convert the updated measurement position into a communication strategy, calculate the fitness value of each quantum capuchin's updated measurement position, and update the local optimal measurement position and the global optimal measurement position; 步骤三五、判断搜索机制是否达到最大迭代次数，若是则输出全局最优测量位置；否则返回步骤三三继续迭代；Step 35: determine whether the search mechanism has reached the maximum number of iterations, if so, output the global optimal measurement position; otherwise, return to step 33 to continue iterating; 步骤三六、将得到的全局最优测量位置根据步骤三二的映射规则转化为相应的信道、编码方式、调制方式和发射功率，获得最优抗干扰决策组合。Step 36: Convert the obtained global optimal measurement position into the corresponding channel, coding mode, modulation mode and transmission power according to the mapping rule of step 32 to obtain the optimal anti-interference decision combination.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107864507A (en) * 2017-11-22 2018-03-30 哈尔滨工程大学 Cognitive ratio power control method based on quantum monkey group hunting mechanism
CN109041073A (en) * 2018-09-01 2018-12-18 哈尔滨工程大学 A kind of optimal node laying method of self energizing wireless sensor network

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107864507A (en) * 2017-11-22 2018-03-30 哈尔滨工程大学 Cognitive ratio power control method based on quantum monkey group hunting mechanism
CN109041073A (en) * 2018-09-01 2018-12-18 哈尔滨工程大学 A kind of optimal node laying method of self energizing wireless sensor network

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