An anti-interference decision-making method and system based on the quantum capuchin monkey mechanism

Abstract

The present invention discloses an anti-interference decision-making method based on the quantum capuchin monkey mechanism, which relates to the technical field of anti-interference decision-making. The technical key points of the present invention include: constructing an anti-interference decision-making model, which is a combination of the channel, coding method, modulation method and transmission power of the communication party; setting an objective function and constraint conditions based on the anti-interference decision-making model; using the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method and transmission power. The quantum capuchin monkey mechanism effectively improves the problems of the original capuchin monkey search mechanism, such as the slow convergence speed and the easy entrapment in local optimal solutions due to the too large search space when solving high-dimensional discrete optimization problems, and breaks through the problem that the original capuchin monkey search mechanism is difficult to handle the coexistence of discrete optimization and continuous optimization. The quantum capuchin monkey mechanism proposed by the present invention has obvious advantages in terms of convergence speed, convergence accuracy and finding the global optimal solution.

Description

Technical Field

[0001] The present invention relates to the field of anti-interference decision-making technology, and in particular to an anti-interference decision-making method and system based on a quantum capuchin mechanism. Background Art

[0002] Anti-interference decision-making methods play an important role in all areas of society, especially in national security. Anti-interference decision-making refers to the communication receiving end obtaining the electromagnetic interference environment and giving feedback to the sending end, making decisions based on the different electromagnetic environments, and selecting the optimal anti-interference strategy to achieve efficient and economical communication while ensuring the safe and reliable transmission of information. For both the communication party and the interference party, anti-interference decision-making is a game process between the two parties. Especially on the battlefield, the interference party does not know what frequency we will use to transmit signals, and most interference signals need to be generated for frequency. Different frequencies will also be interfered to varying degrees due to the occurrence of various situations such as adjacent channel interference, spectrum leakage and multipath effects. Moreover, the higher the transmission power, the better the coding method and modulation method, the more costly support is required. Therefore, the communication party can prepare several frequencies in advance at both ends of the transmitter and receiver for communication, and switch between the frequencies when interfered. If one frequency is affected by interference, it is converted to another frequency that is not interfered with for transmission, but the interference situation in the actual environment is more complicated. In the worst case, all prepared frequencies are affected, and only the frequency with the least impact can be selected for transmission. At the same time, the anti-interference performance can also be improved by adjusting the coding method, modulation method and transmission power. If we consider the possible information protracted war or limited resources in the future, and a series of situations that require economic costs, we can control the economic costs from three aspects: coding method, modulation method and transmission power. Therefore, it is of great significance to design an anti-interference decision-making method that controls economic costs through reasonable resource allocation decisions while meeting the requirements of information transmission rate, and maintains economical and highly reliable signal transmission under the harsh conditions of frequency selectability and multiple interferences at the receiving end.

[0003] After searching the existing literature, it was found that Ran Yu et al. proposed a cognitive anti-interference intelligent decision-making technology based on an improved artificial bee colony mechanism in the Journal of Signal Processing (2019, 35(2), 240-249). This method not only improves the artificial bee colony mechanism, but also uses the improved mechanism to improve the traditional anti-interference method, which improves its global optimization search capability and convergence speed to a certain extent, and makes the average convergence times less and the probability of the optimal solution higher. The intelligent decision-making method of this mechanism includes four aspects: channel, modulation mode, transmission power and interference suppression mode. It uses multi-channel to simulate multi-frequency situations, but there is only one type of interference in each channel, and the complexity is relatively high, so there is room for further discussion and improvement. J. Li et al. published "Satellite communication anti-jamming based on artificial bee colony blind source separation" at the 6th International Conference on Communication (2021, pp. 240-244). They proposed a method of using artificial bee colony optimization mechanism to study the blind source separation problem to conduct satellite communication anti-jamming research, which further solved the defects in spectrum efficiency and anti-jamming performance of the current satellite communication anti-jamming based on spread spectrum and filtering technology. The results showed that it can achieve data transmission under strong interference conditions, but it also only analyzed the situation of single interference and has a high complexity, and there is room for further discussion and improvement.

[0004] The results of existing literature retrieval show that most of the existing anti-interference decision-making methods deal with single-frequency single interference and do not consider economic costs, but the actual interference and game situations are more complicated. If a series of situations that require consideration of economic costs such as information protracted warfare or limited resources occur, economic costs are also one of the issues that must be considered. Summary of the invention

[0005] In view of the above problems, the present invention proposes an anti-interference decision-making method and system based on the quantum capuchin mechanism.

[0006] According to one aspect of the present invention, an anti-interference decision-making method based on a quantum capuchin mechanism is proposed, the method comprising:

[0007] 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;

[0008] Step 2: setting the objective function and constraint conditions based on the anti-interference decision model;

[0009] Step 3: Use the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method, and transmission power.

[0010] Further, the objective function in Step 2 is:

[0011] F(C n , M f , I q , K l ) = w1f ber + w2f s

[0012] where C n represents the nth communication party's optional channel; M f represents the fth communication party's optional modulation method; I q represents the qth communication party's optional transmission power; K l represents the lth communication party's optional coding method; represents the minimum normalized average bit error rate, E max is the maximum average bit error rate, E min is the minimum average bit error rate, and E represents the average bit error rate; represents the minimum normalized transmission power, I max is the maximum transmission power, I min is the minimum transmission power; w1 and w2 are user-expected parameters, and w1 + w2 = 1;

[0013] The constraint conditions are:

[0014] where f R = R b × K l × log2(M f ) represents the information transmission rate, R b is the uncoded original information transmission rate set according to the actual environment during communication, represents the minimum information transmission rate required according to the actual environment during communication.

[0015] Further, the specific steps of Step 3 include:

[0016] Step 3-1: Initialize the quantum positions in the quantum capuchin monkey mechanism;

[0017] Step 3-2: Measure the initial quantum positions of each quantum capuchin monkey, set the fitness function with a penalty function, and calculate the fitness values of all initial quantum positions to determine the local optimal measurement position and the global optimal measurement position;

[0018] Step 33: Different types of quantum capuchin monkeys update the corresponding quantum rotation angles and the measured positions of quantum positions according to their respective update strategies;

[0019] Step 34: Convert the updated measured positions into communication strategies, calculate the fitness values of the updated measured positions of each quantum capuchin monkey, and update the local optimal measured positions and the global optimal measured positions;

[0020] Step 35: Determine whether the search mechanism has reached the maximum number of iterations. If so, output the global optimal measured position; otherwise, return to Step 33 to continue the iteration;

[0021] Step 36: Convert the obtained global optimal measured position into the corresponding channel, coding method, modulation method, and transmission power according to the mapping rule in Step 32 to obtain the optimal anti-interference decision combination.

[0022] Furthermore, the specific steps of Step 31 include: Assume that there are H quantum capuchin monkeys denoted as the quantum capuchin monkey group, and H is taken as an even number; the quantum position of each quantum capuchin monkey has S dimensions, where S is the maximum dimension of the solution space, represents rounding up, N is the total number of channels available to the communicating party, F is the total number of modulation methods available to the communicating party, L is the total number of coding methods available to the communicating party, and Q is the total number of transmission powers available to the communicating party; the quantum position of the h-th quantum capuchin monkey in the t-th generation is where t is the number of iterations, h = 1, 2, …, H, s = 1, 2, …, S; at the initial generation, let t = 1, and each dimension of the quantum positions of the H quantum capuchin monkeys is set to Then the set of quantum positions of the entire quantum capuchin monkey group in the t-th generation is

[0023] Furthermore, the specific steps of Step 32 include:

[0024] The measured position of the h-th quantum capuchin monkey in the t-th generation obtained through measurement is The measurement equation for the s-th dimension quantum position of the h-th quantum capuchin monkey in the t-th generation is where is a random number uniformly distributed between [0, 1], Substitute According to the mapping rule, map it to and substitute it into the anti-interference decision model, where the mapping rule is

[0025] and are respectively the The optional channels of the communication parties, the optional modulation methods of the communication parties, the optional transmission powers of the communication parties, and the optional coding methods of the communication parties;

[0026] Set the penalty function according to the constraint conditions as where ρ is the penalty coefficient; calculate the fitness value with the penalty function of the h-th quantum capuchin monkey in the t-th generation as:

[0027]

[0028] Record the measurement position with the best fitness value of the h-th quantum capuchin monkey up to the t-th generation as the local optimal measurement position Record the measurement position with the best fitness value of all quantum capuchin monkeys up to the t-th generation as the global optimal measurement position

[0029] Furthermore, the specific steps of step three-three include:

[0030] According to the functions of the capuchin monkeys, the first quantum capuchin monkeys in the quantum capuchin monkey group are always used as quantum leading monkeys and quantum companion capuchin monkeys for going out to look for food, denoted as the quantum leading monkey group. Then, the quantum position set of the quantum leading monkey group in the t-th generation is After quantum capuchin monkeys are always used as quantum following monkeys, denoted as the quantum following monkey group. Then, the quantum position set of the quantum following monkey group in the t-th generation is

[0031] For the quantum leading monkey group, set two selection probabilities ε1 and ε2, and then generate a random number uniformly distributed between [0, 1] where ε1 ∈ [0, 1], ε2 ∈ [0, 1], ε1 + ε2 ∈ [0, 1]; if then define the s-th dimensional quantum rotation angle of the th quantum leading monkey in the (t + 1)-th generation as where is the lifespan exponential function, and β0, β1, and β2 are the fixed parameters of the lifespan exponential function, is the s-th dimension of the local optimal measurement position, is the s-th dimension of the global optimal measurement position, s = 1, 2,..., S; if then define the s-th dimensional quantum rotation angle of the th quantum leading monkey in the (t + 1)-th generation as where To randomly select the s-th dimension of the local optimal measurement position of the quantum leading monkey labeled u among other quantum leading monkeys, s = 1, 2, …, S; If Then define the measurement position for the random azimuth food search to be carried out by the th quantum leading monkey in the t-th generation as Define the s-th dimensional quantum rotation angle of the th quantum leading monkey in the (t + 1)-th generation as Where is the s-th dimension of the measurement position for the random azimuth food search to be carried out by the th quantum leading monkey in the t-th generation, and its measurement equation is is a random number uniformly distributed between [0, 1], δ is the random azimuth food search parameter, s = 1, 2, …, S; Define the evolution method of the s-th dimensional quantum position of the th quantum leading monkey in the (t + 1)-th generation as Where is a random number uniformly distributed between [0, 1], c1 represents the probability of qubit mutation when the quantum rotation angle is 0, and its value is a constant between, s = 1, 2, …, S; Obtain the quantum position of the th quantum leading monkey in the (t + 1)-th generation as Where Then the updated set of quantum positions of the quantum leading monkey group in the (t + 1)-th generation is

[0032] For the quantum follower monkey group, define the s-th dimensional quantum rotation angle of the th quantum follower monkey in the (t + 1)-th generation as Where is the s-th dimensional quantum rotation angle of the th quantum leading monkey in the t-th generation, is the s-th dimensional quantum rotation angle of the th quantum follower monkey in the t-th generation, is a random number uniformly distributed between [0, 1], s = 1, 2, …, S. Define the evolution method of the s-th dimensional quantum position of the th quantum follower monkey in the (t + 1)-th generation as Where is a random number uniformly distributed between [0, 1], c2 represents the probability of qubit mutation when the quantum rotation angle is 0, and its value is a constant between, s = 1, 2, …, S; Obtain the quantum position of the The quantum position of the only quantum following monkey is wherein then the set of quantum positions of the quantum following monkey group of the (t + 1)-th generation after update is

[0033] Furthermore, the set of quantum positions of the entire quantum capuchin monkey group of the (t + 1)-th generation after update is obtained as The measurement position is obtained by measuring the s-th dimensional quantum position of the h-th quantum capuchin monkey of the (t + 1)-th generation Its measurement equation is Then the measurement position of the h-th quantum capuchin monkey of the (t + 1)-th generation after measurement is wherein is a random number uniformly distributed between [0, 1]. After measurement, the measurement positions of the H quantum capuchin monkeys of the (t + 1)-th generation are obtained.

[0034] Furthermore, the specific steps of steps three and four include: after the quantum position of the h-th quantum capuchin monkey of the (t + 1)-th generation is measured, is mapped according to the mapping rule to and substituted into the anti-interference decision model to calculate the fitness value with a penalty function The measurement position with the optimal fitness value of the h-th quantum capuchin monkey up to the (t + 1)-th generation is denoted as the local optimal measurement position The measurement position with the optimal fitness value of all quantum capuchin monkeys up to the (t + 1)-th generation is denoted as the global optimal measurement position

[0035] According to another aspect of the present invention, an anti-interference decision system based on a quantum capuchin monkey mechanism is proposed. The system includes:

[0036] A model construction module configured to construct an anti-interference decision model, and 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;

[0037] A target function design module configured to set a target function and constraint conditions based on the anti-interference decision model;

[0038] An optimal decision solving module configured to optimize and solve the target function by using the quantum capuchin monkey mechanism to obtain an optimal combination of a channel, a coding method, a modulation method, and a transmission power.

[0039] Furthermore, the target function in the target function design module is:

[0040] F(C n ,M f ,I q ,K l )=w1f ber+w2f s

[0041] Among them, C n represents the nth communication party's optional channel; M f represents the fth communication party's optional modulation method; I q represents the qth communication party's optional transmission power; K l represents the lth communication party's optional coding method; represents the minimum normalized average bit error rate, E max is the maximum average bit error rate, E min is the minimum average bit error rate, and E represents the average bit error rate; represents the minimum normalized transmission power, I max is the maximum transmission power, I min is the minimum transmission power; w1 and w2 are user-expected parameters, and w1 + w2 = 1;

[0042] The constraint condition is: f R ≥

[0043] Among them, f R = R b ×K l ×log2(M f ) represents the information transmission rate, R b is the uncoded original information transmission rate set according to the actual environment during communication, represents the minimum information transmission rate required according to the actual environment during communication.

[0044] Furthermore, the steps of using the quantum capuchin monkey mechanism to optimize and solve the objective function in the optimal decision-making solving module to obtain the optimal combination of channels, coding methods, modulation methods, and transmission powers include:

[0045] Step 3-1: Initialize the quantum positions in the quantum capuchin monkey mechanism;

[0046] Step 3-2: Measure the initial quantum positions of each quantum capuchin monkey, set the fitness function with a penalty function, and calculate the fitness values of all initial quantum positions to determine the local optimal measurement position and the global optimal measurement position;

[0047] Step 3-3: Different types of quantum capuchin monkeys update the corresponding quantum rotation angles and the measurement positions of the quantum positions according to their respective update strategies;

[0048] Step 3-4: Convert the updated measurement positions into communication strategies, calculate the fitness values of the updated measurement positions of each quantum capuchin monkey, and update the local optimal measurement position and the global optimal measurement position;

[0049] Step 35: Determine whether the search mechanism has reached the maximum number of iterations. If so, output the globally optimal measurement position; otherwise, return to Step 33 to continue the iteration.

[0050] Step 36: Convert the obtained globally optimal measurement position into corresponding channel, coding method, modulation method, and transmit power according to the mapping rule in Step 32 to obtain the optimal anti-interference decision combination.

[0051] The beneficial technical effects of the present invention are:

[0052] In the case of meeting the information transmission rate requirement, the present invention controls the economic cost through the decision of reasonable resource allocation, and in the harsh situation where the frequency is selectable and the receiving end may be subject to multiple interferences, finds an optimal anti-interference decision, that is, the transmit frequency and transmit power, to ensure signal transmission in an economic and highly reliable manner. At the same time, in order to solve the problems of high complexity and large amount of calculation, the anti-interference decision is combined with the intelligent optimization mechanism to reduce the complexity. Compared with the prior art, the advantages of the present invention are:

[0053] 1) The electromagnetic environment faced in the real environment is diverse. Different frequencies may be superimposed with multiple interferences due to different situations such as adjacent channel interference, spectrum leakage, and multipath effect, and the interference power of each interference in each channel is also different. For the transceiver at both ends of the communication party, multiple frequencies can be set as the choices for signal transceiver, and when being interfered, quickly adjust the strategy to switch to a frequency without interference or a frequency with less interference to ensure signal transmission. The present invention simulates a diverse electromagnetic environment, that is, there may be multiple interferences within one frequency and the power of each interference is different. In the case 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 the quantum capuchin monkey mechanism to transmit signals in an economic and highly reliable manner.

[0054] 2) Compared with the original capuchin monkey search mechanism, the quantum capuchin monkey mechanism involved in the present invention effectively improves the problems of too slow convergence speed and easy to fall into local optimal solutions caused by too large search space when the original capuchin monkey search mechanism solves high-dimensional discrete optimization problems, breaks through the problem that the original capuchin monkey search mechanism is difficult to handle the coexistence of discrete optimization and continuous optimization. It can be seen from the simulation results that the quantum capuchin monkey mechanism has obvious superiority over the original capuchin monkey search mechanism in terms of convergence speed, convergence accuracy, and finding the global optimal solution. Compared with some classic swarm intelligence optimization mechanisms such as the improved particle swarm mechanism, the quantum capuchin monkey mechanism improves the search efficiency and can find the global optimal solution with fewer iterations. It can be seen from the simulation results that the quantum capuchin monkey mechanism is significantly superior to the improved particle swarm mechanism in terms of convergence speed. Description of the Drawings

[0055] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, which are included in this specification and form a part of this specification together with the following detailed description, and are used to further illustrate the preferred embodiments of the present invention and explain the principles and advantages of the present invention.

[0056] Figure 1 It is a flowchart of an anti-interference decision-making method based on the quantum capuchin monkey mechanism according to an embodiment of the present invention.

[0057] Figure 2 It is another flowchart of an anti-interference decision-making method based on the quantum capuchin monkey mechanism according to an embodiment of the present invention.

[0058] Figure 3 It is an example diagram of the convergence curves of each optimization method in the case where the user expectations are 0.6 and 0.4 according to an embodiment of the present invention.

[0059] Figure 4 It is an example diagram of the convergence curves of each optimization method in the case where the user expectations are 0.5 and 0.5 according to an embodiment of the present invention.

[0060] Figure 5 It is an example diagram of the convergence curves of each optimization method in the case where the user expectations are 0.4 and 0.6 according to an embodiment of the present invention. Detailed implementation manners

[0061] In order to enable those skilled in the art to better understand the solution of the present invention, the exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are only a part of the embodiments or examples of the present invention, rather than all of them. All other embodiments or examples obtained by those of ordinary skill in the art based on the embodiments or examples in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] The present invention considers that in the case of meeting the information transmission rate requirements, by making decisions on reasonable resource allocation to control the economic cost, aiming at frequency selectivity and in the harsh case where the receiving end may be subject to multiple interferences, finding an optimal anti-interference decision to ensure signal transmission in an economic and highly reliable manner, and thus designs an anti-interference decision-making method based on the quantum capuchin monkey mechanism. Among them, the quantum optimization theory is used, and a new quantum rotation angle is utilized, that is, the degree of change of the quantum rotation angle is combined with the lifetime exponential function of the capuchin monkey search mechanism to achieve the balance between global and local searches, effectively improving the disadvantages of the existing capuchin monkey search mechanism in dealing with high-dimensional discrete optimization problems, such as not only having too slow convergence speed, too large search space, but also being easily trapped in local optimal solutions, and breaking through the problem that the existing capuchin monkey search mechanism cannot solve the problems of discrete optimization and continuous optimization existing simultaneously.

[0063] An embodiment of the present invention proposes an anti-interference decision-making method based on a quantum capuchin monkey mechanism, as Figures 1-2 shown. The method includes:

[0064] Step 1: Construct an anti-interference decision-making model, where the anti-interference decision-making model is a combination of the communication party's channel, coding method, modulation method, and transmission power;

[0065] Step 2: Set an objective function and constraint conditions based on the anti-interference decision-making model;

[0066] Step 3: Use the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method, and transmission power; specifically including:

[0067] Step 3-1: Initialize the quantum position in the quantum capuchin monkey mechanism;

[0068] Step 3-2: Measure the initial quantum positions of each quantum capuchin monkey, set a fitness function with a penalty function, and calculate the fitness values of all initial quantum positions to determine the local optimal measurement position and the global optimal measurement position;

[0069] Step 3-3: Different types of quantum capuchin monkeys update their corresponding quantum rotation angles and the measurement positions of the quantum positions according to their respective update strategies;

[0070] Step 3-4: Convert the updated measurement positions into communication strategies, calculate the fitness values of the updated measurement positions of each quantum capuchin monkey, and update the local optimal measurement position and the global optimal measurement position;

[0071] Step 3-5: Determine whether the search mechanism has reached the maximum number of iterations. If so, output the global optimal measurement position; otherwise, return to Step 3-3 to continue the iteration.

[0072] Step 3-6: Convert the obtained global optimal measurement position into the corresponding channel, coding method, modulation method, and transmission power according to the mapping rule in Step 3-2 to obtain the optimal anti-interference decision-making combination.

[0073] The method starts from Step 1. In Step 1, an anti-interference decision-making model is constructed, where the anti-interference decision-making model is a combination of the communication party's channel, coding method, modulation method, and transmission power.

[0074] According to the embodiment of the present invention, as Figure 2 shown, an intelligent anti-interference decision-making model is established to determine the key parameters of the communication corresponding to the quantum capuchin monkey mechanism. Set the communication party's optional channel vector as C = [C1, C2,..., C N , where N is the total number of optional channels of the communication party, C nThe nth communication - party - selectable channel selected for decision - making, where n = 1, 2, …, N; the communication - party - selectable modulation - mode vector is M = [M1, M2, …, M F , where F is the total number of communication - party - selectable modulation modes, and M f is the fth communication - party - selectable modulation mode selected for decision - making, where f = 1, 2, …, F; the communication - party - selectable coding - mode vector is K = [K1, K2, …, K L , where L is the total number of communication - party - selectable coding modes, and K l is the lth communication - party - selectable coding mode selected for decision - making, where l = 1, 2, …, L; the communication - party - selectable transmit - power vector is I = [I1, I2, …, I Q , where Q is the total number of communication - party - selectable transmit powers, and I q is the qth communication - party - selectable transmit power selected for decision - making, where q = 1, 2, …, Q; the jammer - selectable interference - signal vector is Y = [Y1, Y2, …, Y A , where A is the total number of jammer - selectable interference signals, and Y a is the ath jammer - selectable interference signal, where a = 1, 2, …, A; the jammer - selectable interference - power vector is J = [J1, J2, …, J B , where B is the total number of jammer - selectable interference powers, and J b is the bth jammer - selectable interference power, where b = 1, 2, …, B.

[0075] The probability that various interference signals appear in the nth channel is where is the appearance probability of the ath jammer - selectable interference signal in the nth communication - party - selectable channel, where n = 1, 2, …, N. Subsequently, a random number uniformly distributed between [0, 1] is generated for each interference signal in the nth channel where is the random number uniformly distributed between [0, 1] generated for the ath jammer - selectable interference signal in the nth communication - party - selectable channel, where n = 1, 2, …, N. If is less than it indicates that there is interference of Y a in the nth channel, otherwise there is no interference, and each interference will randomly select an interference power J b for transmission.

[0076] The intelligent anti - interference decision - making selection is divided into four aspects: the channel vector C, the coding - mode vector K, the modulation - mode vector M, and the transmit - power vector I. Different combinations of channels, coding modes, modulation modes, and transmit powers can obtain different anti - interference strategies D o =[C n , M f, I q , K l ], where \(o = 1, 2, \ldots, p\), \(p = N\times F\times Q\times \ldots\), and the anti-interference decision set is \(D=\{D_1, D_2, \ldots, D p \}\). Encode the randomly generated information at the coding rate \(K l and modulate the encoded information in the modulation mode \(M f and then transmit the signal with the transmission power \(I q to the channel \(C n for transmission. There may be various interference signals in the channel and each interference signal has a different interference power. After the receiving end receives the signal, demodulate and decode it and compare it with the original information to obtain the bit error rate \(E_1\). The bit error rates obtained by calculating multiple times under the same simulation conditions are respectively denoted as \(E_2, E_3, \ldots, E m-1 and \(E m , and the average bit error rate obtained by using the anti-interference method selected by the decision is denoted as to reduce the contingency of the interference signal being too large or too small, and \(m\) is the number of calculations under the same simulation conditions.

[0077] Then execute Step 2. In Step 2, set the objective function and constraint conditions based on the anti-interference decision model.

[0078] According to the embodiments of the present invention, starting from the communication system metrics, in order to achieve an economical and highly reliable transmission mode, set the objective function with the minimum normalized average bit error rate and the minimum normalized transmission power. Set the objective function as:

[0079] \(F(C n , M f , I q , K l ) = w_1f ber + w_2f s

[0080] where, is the minimum normalized average bit error rate obtained by using the anti-interference method selected by the decision, \(E max is the maximum average bit error rate, \(E min is the minimum average bit error rate, is the minimum normalized transmission power obtained by using the anti-interference method selected by the decision, \(I max is the maximum transmission power, \(I minis the minimum transmission power, w1 and w2 are user-expected parameters, and w1 + w2 = 1. Since the average bit error rate and the transmission power have different magnitudes and units, they are each normalized to unify the dimensions, so as to obtain a reasonable value to evaluate the quality of the strategy. The user-expected parameters refer to the importance that the user can allocate to the average bit error rate and the transmission power according to their own needs.

[0081] In the case of meeting the information transmission rate requirement, the economic cost is controlled through the decision of reasonable resource allocation, so as to be used in a series of situations that require considering the economic cost, such as limited resources or information protracted war. Therefore, the constraint conditions are set as:

[0082]

[0083] where f R = R b ×K l ×log2(M f ) 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 during communication.

[0084] Then, step three is executed. In step three, the quantum capuchin mechanism is used to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method, and transmission power.

[0085] First, in step 3.1, the quantum positions in the quantum capuchin mechanism are initialized.

[0086] According to the embodiments of the present invention, it is assumed that there are H quantum capuchins denoted as the quantum capuchin group, and H is taken as an even number. The quantum position of each quantum capuchin has S dimensions, where S is the maximum dimension of the solution space, represents rounding up. Each quantum capuchin has its own quantum position. Then, the quantum position of the h-th quantum capuchin in the t-th generation is where t is the number of iterations, T is the maximum number of iterations, h = 1, 2,..., H, s = 1, 2,..., S. At the initial generation, let t = 1, and each dimension of the quantum positions of the H quantum capuchins is set to Then, the set of quantum positions of the entire quantum capuchin group in the t-th generation is

[0087] Then, in step 32, measure the initial quantum positions of each quantum capuchin monkey, set a fitness function with penalty and calculate the fitness values of all initial positions to determine the local optimal measurement position and the global optimal measurement position.

[0088] According to an embodiment of the present invention, the measured position of the h-th quantum capuchin monkey in the t-th generation obtained by measurement is The measurement equation for the s-th dimensional quantum position of the h-th quantum capuchin monkey in the t-th generation is where is a random number uniformly distributed between [0, 1], h = 1, 2, …, H, s = 1, 2, …, S. Map to an anti-interference method according to the mapping rule and substitute it into the anti-interference decision-making model, where the mapping rule is and are respectively the th communication party's optional channel, the th communication party's optional modulation method, the th communication party's optional transmission power, and the th communication party's optional coding method selected by the h-th quantum capuchin monkey in the t-th generation.

[0089] Set the penalty function according to the constraint condition as where ρ is the penalty coefficient.

[0090] Calculate the fitness value with penalty of the h-th quantum capuchin monkey in the t-th generation as:

[0091]

[0092] Record the measurement position with the best fitness value of the h-th quantum capuchin monkey up to the t-th generation as the local optimal measurement position Record the measurement position with the best fitness value of all quantum capuchin monkeys up to the t-th generation as the global optimal measurement position

[0093] Then, in step 33, different types of quantum capuchin monkeys update their corresponding quantum rotation angles and the measurement positions of quantum positions according to their respective update strategies.

[0094] According to an embodiment of the present invention, according to the functions of the capuchin monkeys, the first quantum capuchin monkeys in the quantum capuchin monkey group are always used as quantum leader monkeys and quantum companion capuchin monkeys going out to look for food, denoted as the quantum leader monkey group. Then, the set of quantum positions of the quantum leader monkey group in the t-th generation is The latter Only quantum capuchin monkeys always act as quantum follower monkeys, denoted as the quantum follower monkey group. Then, the quantum position set of the t-th generation of quantum follower monkey groups is

[0095] For the quantum leader monkey group, two selection probabilities ε1 and ε2 are set. Subsequently, a random number uniformly distributed between [0, 1] is generated where ε1 ∈ [0, 1], ε2 ∈ [0, 1], and ε1 + ε2 ∈ [0, 1]. If then the s-th dimensional quantum rotation angle of the th quantum leader monkey in the (t + 1)-th generation is defined as where is the lifespan exponential function, and β0, β1, and β2 are fixed parameters of the lifespan exponential function. is the s-th dimension of the local optimal measurement position. is the s-th dimension of the global optimal measurement position. s = 1, 2, …, S; if then the s-th dimensional quantum rotation angle of the th quantum leader monkey in the (t + 1)-th generation is defined as where is the s-th dimension of the local optimal measurement position of the quantum leader monkey with label u randomly selected from other quantum leader monkeys. s = 1, 2, …, S; if then the measurement position for the random azimuth foraging to be performed by the th quantum leader monkey in the t-th generation is Define the s-th dimensional quantum rotation angle of the th quantum leader monkey in the (t + 1)-th generation as where is the s-th dimension of the measurement position for the random azimuth foraging to be performed by the th quantum leader monkey in the t-th generation. Its measurement equation is is a random number uniformly distributed between [0, 1], and δ is the random azimuth foraging parameter. s = 1, 2, …, S. Define the evolution method of the s-th dimensional quantum position of the th quantum leader monkey in the (t + 1)-th generation as where is a random number uniformly distributed between [0, 1], and c1 represents the probability of qubit mutation when the quantum rotation angle is 0. Its value is a constant between s = 1, 2, …, S. Obtain the quantum position of the th quantum leader monkey in the (t + 1)-th generation as where Then the quantum position set of the (t + 1)-th generation of quantum leader monkeys after update is

[0096] For the quantum follower monkeys, define the s-th dimensional quantum rotation angle of the -th quantum follower monkey in the (t + 1)-th generation as where is the s-th dimensional quantum rotation angle of the -th quantum leader monkey in the t-th generation, is the s-th dimensional quantum rotation angle of the -th quantum follower monkey in the t-th generation, is a random number uniformly distributed between [0, 1], s = 1, 2, …, S. Define the evolution method of the s-th dimensional quantum position of the -th quantum follower monkey in the (t + 1)-th generation as where is a random number uniformly distributed between [0, 1], c2 represents the probability of qubit mutation when the quantum rotation angle is 0, and its value is a constant between, s = 1, 2, …, S. The quantum position of the -th quantum follower monkey in the (t + 1)-th generation is obtained as where Then the quantum position set of the (t + 1)-th generation of quantum follower monkeys after update is

[0097] Furthermore, the quantum position set of the entire (t + 1)-th generation of quantum capuchin monkeys after update is obtained as The measurement position is obtained by measuring the s-th dimensional quantum position of the h-th quantum capuchin monkey in the (t + 1)-th generation and its measurement equation is Then the measurement position of the h-th quantum capuchin monkey in the (t + 1)-th generation after measurement is where is a random number uniformly distributed between [0, 1], h = 1, 2, …, H, s = 1, 2, …, S, and the measurement positions of H quantum capuchin monkeys in the (t + 1)-th generation are obtained after measurement.

[0098] Then, in steps three and four, the updated measurement positions are converted into communication strategies, the fitness values of the updated measurement positions of each quantum capuchin monkey are calculated, and the local optimal measurement position and the global optimal measurement position are updated.

[0099] According to the embodiment of the present invention, after the quantum position of the h-th quantum capuchin monkey in the (t + 1)-th generation is measured, it is mapped to an anti-interference method according to the mapping rule and substituted into the anti-interference decision model to calculate the fitness value with penalty where \(h = 1, 2, \ldots, H\). Denote the measurement position with the optimal fitness value among the first \(h\) quantum capuchin monkeys up to the \((t + 1)\) - th generation as the local - optimal measurement position. Denote the measurement position with the optimal fitness value among all quantum capuchin monkeys up to the \((t + 1)\) - th generation as the global - optimal measurement position.

[0100] Then, in step 3 - 5, determine whether the search mechanism has reached the maximum number of iterations. If so, output the global - optimal measurement position; otherwise, let \(t=t + 1\), return to step 3 - 3, and continue the iteration.

[0101] Then, in step 3 - 6, convert the obtained global - optimal measurement position into the corresponding channel, coding method, modulation method, and transmission power according to the mapping rule in step 3 - 2 to obtain the optimal anti - interference decision combination.

[0102] Further, verify the technical effect of the present invention through experiments.

[0103] Denote the anti - interference decision method of the quantum capuchin monkey mechanism proposed in the present invention as QCapSA. The search mechanisms for comparison are the anti - interference decision method of the capuchin monkey search mechanism and the anti - interference decision method of the improved particle swarm mechanism, which are denoted as CapSA and IPO - PSO respectively.

[0104] To comprehensively compare the performance of the three methods, perform the same initialization on QCapSA, CapSA, and IPO - PSO, and set the same communication system parameters: R b = 10 7 bit / s; \(N = 4\); \(F = 4\), which are BPSK, QPSK, 16QAM, and 64QAM respectively; \(L = 4\), which are 1 - coding (i.e., non - coding), coding, coding, and coding; \(Q = 64\), the transmission power range is from - 11dB to 20.5dB, and the resolution is 0.5dB; \(E\) max = 0.5, \(E\) min = 10 -6 ; \(I\) max = 20.5, \(I\) min = - 11; \(B = 10\), the interference power range of the interferer is from 21dB to 30dB, and the resolution is 1dB; \(A = 3\), which are 50% partial - band interference, 10% comb - like interference, and 10% single - tone swept - frequency interference, and the probability of each interference occurring in each channel is

[0105] Set \(S = 12\) and the penalty function \(\rho = 10\) in QCapSA -7 , ε1 = 0.8, ε2 = 0.1, β0 = 2, β1 = 21, β2 = 2, δ = 0.5. After observing the quantum capuchin monkeys, the first two dimensions correspond to channels, where "00" represents the first channel, "01" represents the second channel, "10" represents the third channel, and "11" represents the fourth channel; the third and fourth dimensions represent modulation methods, where "00" represents BPSK, "01" represents QPSK, "10" represents 16QAM, and "11" represents 64QAM; the fifth to tenth dimensions correspond to transmit power, where "000000" represents -11 dB, "000001" represents -10.5 dB, and so on until "111111" represents 20.5 dB, with a resolution of 0.5 dB; the last two dimensions represent coding methods, where "00" represents 1 coding, "01" represents coding, "10" represents coding, and "11" represents coding. The population sizes and maximum iteration numbers of the three mechanisms are the same, which are H = 20 and T = 1000 respectively. The average fitness values of 500 runs are taken to plot the fitness curve. Other relevant parameter settings of CapSA can be found in "A novel meta - heuristic search algorithm for solving optimization problems: capuchin search algorithm" published by Malik Braik et al. in 《Neural Computing and Applications》(2021, Volume 33, pages 2515–2547). Other relevant parameter settings of IPO - PSO can be found in "Communication anti - interference decision engine based on initial population - optimized particle swarm mechanism" published by Hui Xianyang et al. in 《Communications Technology》(2015, Volume 48, No.7).

[0106] Figures 3-5 Examples of the convergence curves of each optimization method are given for different user - expected parameters. It can be seen from this that QCapSA has obvious superiority over CapSA in terms of convergence speed, convergence accuracy, and finding the global optimal solution. Although IPO - PSO can also find the global optimal solution in more simulation runs, it is obvious that its convergence speed is much weaker than that of QCapSA.

[0107] Another embodiment of the present invention proposes an anti - interference decision system based on the quantum capuchin monkey mechanism. The system includes:

[0108] A model construction module configured to construct an anti - interference decision model, where the anti - interference decision model is a combination of the channel, coding method, modulation method, and transmit power of the communication party;

[0109] A target function design module configured to set a target function and constraint conditions based on the anti-interference decision model;

[0110] An optimal decision solving module configured to optimize and solve the target function by using the quantum capuchin monkey mechanism to obtain an optimal combination of a channel, a coding method, a modulation method, and a transmission power.

[0111] In this embodiment, preferably, the target function in the target function design module is:

[0112] F(C n ,M f ,I q ,K l ) = w1f ber +w2f s

[0113] Wherein, C n represents the nth communication party's optional channel; M f represents the fth communication party's optional modulation method; I q represents the qth communication party's optional transmission power; K l represents the lth communication party's optional coding method; represents the minimum normalized average bit error rate, E max is the maximum average bit error rate, E min is the minimum average bit error rate, and E represents the average bit error rate; represents the minimum normalized transmission power, I max is the maximum transmission power, I min is the minimum transmission power; w1 and w2 are user-expected parameters, and w1 + w2 = 1;

[0114] The constraint conditions are:

[0115] Wherein, f R = R b ×K l ×log2(M f ) represents the information transmission rate, R b is the unencoded original information transmission rate set according to the actual environment during communication, represents the minimum information transmission rate required according to the actual environment during communication.

[0116] In this embodiment, preferably, the steps of using the quantum capuchin monkey mechanism in the optimal decision solving module to optimize and solve the target function to obtain an optimal combination of a channel, a coding method, a modulation method, and a transmission power include:

[0117] Step 3-1: Initialize the quantum position in the quantum capuchin monkey mechanism;

[0118] Step 32: Measure the initial quantum positions of each quantum capuchin monkey, set a fitness function with a penalty function, and calculate the fitness values of all initial quantum positions to determine the local optimal measurement position and the global optimal measurement position;

[0119] Step 33: Different types of quantum capuchin monkeys update their corresponding quantum rotation angles and the measurement positions of quantum positions according to their respective update strategies;

[0120] Step 34: Convert the updated measurement positions into communication strategies, calculate the fitness values of the updated measurement positions of each quantum capuchin monkey, and update the local optimal measurement position and the global optimal measurement position;

[0121] 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 the iteration;

[0122] Step 36: Convert the obtained global optimal measurement position into the corresponding channel, coding method, modulation method, and transmit power according to the mapping rule in Step 32 to obtain the optimal anti-interference decision combination.

[0123] The functions of the anti-interference decision system based on the quantum capuchin monkey mechanism described in the embodiments of the present invention can be illustrated by the aforementioned anti-interference decision method based on the quantum capuchin monkey mechanism. Therefore, for the parts not detailed in the system embodiments, reference can be made to the above method embodiments and will not be elaborated here.

[0124] Although the present invention has been described based on a limited number of embodiments, those skilled in the art in this technical field understand that other embodiments can be envisioned within the scope of the present invention as thus described. For the scope of the present invention, the disclosure of the present invention is illustrative rather than restrictive, and the scope of the present invention is defined by the appended claims.

Claims

1. An anti-interference decision-making method based on the quantum capuchin monkey mechanism, characterized in that Including: Step 1: Construct an anti-interference decision model, where the anti-interference decision model is a combination of the communication party's channel, coding method, modulation method, and transmission power; Step 2: Set the objective function and constraint conditions based on the anti-interference decision model; the objective function is: ； Among them, represents the th communication party's optional channel; represents the th communication party's optional modulation method; represents the th communication party's optional transmission power; represents the th communication party's optional coding method; 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; represents the minimum normalized transmission power, is the maximum transmission power, is the minimum transmission power; and are user-expected parameters, ; The constraint conditions are as follows: ; Among them, represents the information transmission rate, which is the uncoded original information transmission rate set according to the actual environment during communication, represents the minimum information transmission rate required according to the actual environment during communication; Step 3: Use the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method, and transmission power; including: Step 3-1: Initialize the quantum positions in the quantum capuchin monkey mechanism; Step 3-2: Measure the initial quantum positions of each quantum capuchin monkey, 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; Step 3-3: Different types of quantum capuchin monkeys update the corresponding quantum rotation angles and the measurement positions of the quantum positions according to their respective update strategies; Step 3-4: Convert the updated measurement positions into communication strategies, calculate the fitness values of the measurement positions after the update of each quantum capuchin monkey, and update the local optimal measurement position and the global optimal measurement position; Step 3-5: Determine whether the search mechanism has reached the maximum number of iterations. If so, output the global optimal measurement position; otherwise, return to Step 3-3 to continue the iteration; Step 3-6: Convert the obtained global optimal measurement position into the corresponding channel, coding method, modulation method, and transmission power according to the mapping rule in Step 3-2 to obtain the optimal anti-interference decision combination.

2. The anti-interference decision-making method based on the quantum capuchin monkey mechanism according to claim 1, wherein The specific steps of Step 3-1 include: Assume that there exists only quantum capuchin monkeys are recorded as a quantum capuchin monkey group, is taken as an even number; the quantum position of each quantum capuchin monkey has dimensions, where is the maximum dimension of the solution space, represents rounding up, is the total number of optional channels for the communicating party, is the total number of optional modulation methods for the communicating party, is the total number of optional coding methods for the communicating party, is the total number of optional transmit powers for the communicating party; the th generation, the nd quantum capuchin monkey's quantum position is , where is the number of iterations, At the initial generation, let the dimensions of the quantum positions of the nd quantum capuchin monkeys be set to each, then the set of quantum positions of the entire quantum capuchin monkey group in the th generation is 3. The anti-interference decision-making method based on the quantum capuchin monkey mechanism according to claim 2, wherein, The specific steps of Step 3-2 include: The measurement position of the th quantum capuchin monkey obtained by measurement is , and the measurement equation of the th quantum capuchin monkey's -dimensional quantum position is , where is a random number uniformly distributed between [0, 1], ; is mapped to according to the mapping rule and substituted into the anti-interference decision-making model, where the mapping rule is , and are respectively the th quantum capuchin monkey's selected th communication party's available channel, th communication party's available modulation method, th communication party's available transmission power, and th communication party's available coding method; Set the penalty function according to the constraint conditions as , where is the penalty coefficient; calculate the fitness value with the penalty function of the -th generation of the -th quantum capuchin monkey as follows: ； Record the measurement position with the optimal fitness value among the quantum capuchin monkeys up to the generation as the local optimal measurement position , and record the measurement position with the optimal fitness value among all the quantum capuchin monkeys up to the generation as the global optimal measurement position .

4. The anti-interference decision-making method based on the quantum capuchin monkey mechanism according to claim 3, characterized in that, The specific steps of Step 3-3 include: According to the functions of capuchin monkeys, the first quantum capuchin monkeys in the quantum capuchin monkey group always serve as the quantum leading monkeys and quantum companion capuchin monkeys for going out to look for food, which are recorded as the quantum leading monkey group. Then, the quantum position set of the th-generation quantum leading monkey group is ; The subsequent quantum capuchin monkeys always serve as quantum follower monkeys, which are recorded as the quantum follower monkey group. Then, the quantum position set of the th-generation quantum follower monkey group is ; For the quantum leadership monkey group, set two selection probabilities and , and then generate a random number that follows a uniform distribution between [0, 1], where ; if , then define the -th generation, the -th quantum leadership monkey's -th dimensional quantum rotation angle as , where is the lifetime exponential function, and are the fixed parameters of the lifetime exponential function, is the -th dimension of the local optimal measurement position, is the -th dimension of the global optimal measurement position, ; if , then define the -th generation, the -th quantum leadership monkey's -th dimensional quantum rotation angle as , where is the -th dimension of the local optimal measurement position of the quantum leadership monkey randomly selected from other quantum leadership monkeys with label , ; if , then define the measurement position of the -th generation, the -th quantum leadership monkey for random azimuth foraging as , define the -th generation, the -th quantum leadership monkey's -th dimensional quantum rotation angle as , where is the -th generation, the -th dimension of the measurement position of the -th dimensional quantum leadership monkey for random azimuth foraging, and its measurement equation is , is a random number that follows a uniform distribution between [0, 1], is the random azimuth foraging parameter, ; define the evolution method of the -th generation, the -th quantum leadership monkey's -th dimensional quantum position as , where is a random number that follows a uniform distribution between [0, 1], represents the probability of qubit mutation when the quantum rotation angle is 0, and its value is constant between ; obtain the generation of the quantum position of the th quantum leading monkey is ; then the quantum position set of the quantum leading monkey group in the th generation after update is ; For the quantum following monkey group, define the -th generation and the -th quantum following monkey's -dimensional quantum rotation angle as , where is the -th generation and the -th quantum leading monkey's -dimensional quantum rotation angle, is the -th generation and the -th quantum following monkey's -dimensional quantum rotation angle, is a random number uniformly distributed between [0, 1], ; define the evolution method of the -th generation and the -th quantum following monkey's -dimensional quantum position as , where is a random number uniformly distributed between [0, 1], represents the probability of qubit mutation when the quantum rotation angle is 0, and its value is a constant between , ; obtain the quantum position of the -th generation and the -th quantum following monkey as , where ; then the updated quantum position set of the -th generation of quantum following monkey group is ; Furthermore, the quantum position set of the entire quantum capuchin monkey group after the -th generation update is ; By measuring the -th generation and the -th quantum capuchin monkey's -dimensional quantum position, the measured position is obtained. Its measurement equation is . Then, the measured position of the -th generation and the -th quantum capuchin monkey after measurement is , where is a random number uniformly distributed between [0, 1]. After measurement, the measured positions of quantum capuchin monkeys in the -th generation are obtained.

5. The anti-interference decision method based on the quantum capuchin monkey mechanism according to claim 4, wherein The specific steps of Steps 3 and 4 include: After the quantum positions of the th quantum capuchin monkeys are measured, they will be mapped according to the mapping rule to and substituted into the anti-interference decision model to calculate the fitness value with a penalty function ; The measurement position with the optimal fitness value of the th quantum capuchin monkey up to the th generation is recorded as the local optimal measurement position , and the measurement position with the optimal fitness value of all quantum capuchin monkeys up to the th generation is recorded as the global optimal measurement position .

6. An anti-interference decision-making system based on the quantum capuchin monkey mechanism, characterized in that, Including: A model construction module configured to construct an anti-interference decision model, where the anti-interference decision model is a combination of the communication party's channel, coding method, modulation method, and transmission power; An objective function design module configured to set the objective function and constraint conditions based on the anti-interference decision model; the objective function in the objective function design module is: ； Among them, represents the th communication party's optional channel; represents the th communication party's optional modulation method; represents the th communication party's optional transmission power; represents the th communication party's optional coding method; 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; represents the minimum normalized transmit power, is the maximum transmit power, is the minimum transmit power; and are user-expected parameters, ; The constraint conditions are as follows: ; Among them, represents the information transmission rate, which is the uncoded original information transmission rate set according to the actual environment during communication, represents the minimum information transmission rate required according to the actual environment during communication; An optimal decision solving module configured to use the quantum capuchin monkey mechanism to optimize and solve the objective function to obtain the optimal combination of the channel, coding method, modulation method, and transmission power, including: Step 3-1: Initialize the quantum positions in the quantum capuchin monkey mechanism; Step 3-2: Measure the initial quantum positions of each quantum capuchin monkey, 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; Step 3-3: Different types of quantum capuchin monkeys update the corresponding quantum rotation angles and the measurement positions of the quantum positions according to their respective update strategies; Step 3-4: Convert the updated measurement positions into communication strategies, calculate the fitness values of the measurement positions after the update of each quantum capuchin monkey, and update the local optimal measurement position and the global optimal measurement position; Step 3-5: Determine whether the search mechanism has reached the maximum number of iterations. If so, output the global optimal measurement position; otherwise, return to Step 3-3 to continue the iteration; Step 3-6: Convert the obtained global optimal measurement position into the corresponding channel, coding method, modulation method, and transmission power according to the mapping rule in Step 3-2 to obtain the optimal anti-interference decision combination.

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