CN111314982A - A Heterogeneous Private Network Vertical Handoff Method Based on Speed Pre-judgment and Fuzzy Logic - Google Patents

Abstract

The present invention proposes a method for vertical switching of heterogeneous private networks based on speed pre-judgment and fuzzy logic. value, the unqualified network will be eliminated in advance; step 2, fuzzification processing: in the qualified network, the network parameters are mapped to three fuzzy sets of low, medium and high according to the membership function, and then the membership is normalized and quantified to calculate each Standardized membership value of attributes; Step 3, network performance evaluation value calculation: Calculate the weight of each attribute, use the speed score value and the attribute standardized membership value to calculate the comprehensive performance evaluation value of each candidate network, and select the candidate with the largest comprehensive performance evaluation value. network to switch. Compared with the traditional vertical handover algorithm based on RSS, the present invention can significantly reduce the number of handovers and has better performance.

Description

A Heterogeneous Private Network Vertical Handoff Method Based on Speed Pre-judgment and Fuzzy Logic

technical field

The invention relates to a vertical switching method for heterogeneous private networks based on speed pre-judgment and fuzzy logic, and belongs to the field of communication networks.

Background technique

A dedicated mobile communication network refers to a wireless communication network that provides command and dispatch services for specific user groups to meet communication requirements under special circumstances. With the development of wireless communication technology, the 5G era in public networks is coming soon, and LTE technology is quite mature, which can transmit multimedia video services very quickly. In this context, private network communication is no longer only satisfied with voice communication services. And generate richer business requirements. China's police mobile communication network is seeking the interconnection between narrowband private network police digital trunking (PDT) and broadband trunking communications private network (B-TrunC). "Transmission + trunking voice communication" private network broadband trunking system standard, which is compatible with LTE data services and adds broadband trunking services such as multimedia trunking scheduling. In the future, the dedicated mobile communication network will inevitably evolve towards the direction of large bandwidth and high speed, and realizing "big integration, high sharing, deep application, and intelligence" has become the goal of China's public security mobile informatization. Handover refers to the mechanism and process in which the current connection between the mobile terminal and each network is transferred from one access point to another. switch. The horizontal handover refers to the handover between different access points under the same network technology, and the vertical handover refers to the handover between the access points of different network technologies. Therefore, the vertical handover is an indispensable key technology in a heterogeneous network system. The vertical handover process is divided into three stages: network discovery, handover judgment, and handover execution. Among them, handover judgment is the most important link. The task of this stage is to judge whether handover needs to be performed according to the obtained handover judgment indicators, and which target should be selected. network. Whether the handover decision algorithm is efficient and reasonable will directly affect the performance of the network.

At present, the more mature decision algorithms in the vertical handover decision process of heterogeneous wireless networks mainly include traditional handover decision algorithms based on received signal strength (RSS) and handover decision algorithms based on multi-attribute decision-making. With the rapid development of artificial intelligence technology, many The industry is exploring the use of artificial intelligence to solve technical problems in various fields, so in recent years, many vertical switching judgment algorithms based on artificial intelligence and neural networks have also been produced. The application scenarios of most of the algorithms are mostly network switching of personal mobile terminals in heterogeneous networks, and terminal devices are basically mobile phones carried by people. However, in private network communication, mobile stations include handheld mobile stations and vehicle-mounted mobile stations. For vehicle-mounted mobile stations, the range of speed changes is large. The heterogeneous private network includes PDT and B-TrunC networks, and their base station coverage radius is very different. Therefore, when the terminal moves fast, the handover effect of the existing handover decision algorithm is not very good. . In addition to the traditional handover decision method, experts and scholars have also done a lot of research on the application of fuzzy logic in the vertical handover process. In practical problems, there are many variables that are difficult to quantify accurately, such as the magnitude of the terminal moving speed involved in the vertical handover decision problem, the strength of the received signal, and the like. Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory, which is mostly used to deal with approximate reasoning problems. Therefore, using fuzzy logic to describe and deal with some ambiguous problems will have better results. The fuzzy logic method includes three steps: fuzzification process, fuzzy reasoning process and defuzzification process. Many existing algorithms combine fuzzy logic with neural networks, integrating fuzzy reasoning, distributed processing and self-learning capabilities. Either the neural network or the neural network will take a long time, so the delay of the decision is relatively large.

SUMMARY OF THE INVENTION

The purpose of the present invention is to propose a vertical handover method for heterogeneous private networks based on speed pre-judgment and fuzzy logic. Then use fuzzy theory to deal with some network properties with strong uncertainty, and finally consider network properties and speed scores when calculating network performance evaluation value, so that the calculation results are more reasonable and reliable.

A method for vertical handover of heterogeneous private networks based on speed pre-judgment and fuzzy logic, the method for vertical handover of heterogeneous private networks comprises the following steps:

Step 1. Speed pre-judgment: According to the different network characteristics of the PDT network and the B-TrunC network and their sensitivity to the speed of the mobile terminal, set different speed pre-judgment curve models, and obtain the network evaluation value according to the real-time speed of the terminal. Eligible networks are eliminated in advance;

Step 2. Fuzzification processing: In a qualified network, map the network parameters to three fuzzy sets of low, medium and high according to the membership function, and then normalize and quantify the membership to calculate the standardized membership value of each attribute for subsequent use. calculate;

Step 3. Calculation of network performance evaluation value: First, use the AHP to calculate the weight of each attribute, and then use the speed score value obtained in step 1 and the attribute standardized membership value obtained in step 2 to calculate the comprehensive performance evaluation of each candidate network. value, and finally select the candidate network with the largest comprehensive performance evaluation value for handover.

Further, in step 1, the speed pre-decision function of the PDT network is shown in formula (8):

The speed pre-decision function of the C-TrunC network is shown in formula (9):

和

分别表示PDT网络和B-TrunC网络能够接受的终端最大移动速度，当速度大于此门限时，该网络的速度预判决得分为0，则提前将此网络从候选网络集合中删除；若速度预判决得分大于0，则保留此网络在候选网络集合中，同时将速度得分VS作为输入参与步骤三中网络性能评估值的计算。where the decision threshold

and

Represents the maximum moving speed of the terminal that can be accepted by the PDT network and the B-TrunC network. When the speed is greater than this threshold, the network's speed pre-judgment score is 0, and the network will be deleted from the candidate network set in advance; if the speed pre-judgment is 0 If the score is greater than 0, the network is kept in the candidate network set, and the speed score VS is used as an input to participate in the calculation of the network performance evaluation value in step 3.

Further, in step 2, the following steps are specifically included:

Step 21. Input parameter fuzzification processing:

Using RSS and using bandwidth B for fuzzification processing, the fuzzification processing method is: according to the membership function formulas (10), (11) and (12), the parameter value x of the attribute K is mapped to low, medium and high fuzzy Set, get the membership vector

Step 22. Normalization and quantification of membership degree:

The parameter value of attribute K is normalized to three fuzzy sets of low, medium and high, and the normalized vector is obtained. The rules are shown in formula (13):

Step 23: Calculate the standardized membership value:

According to the membership degree vector and the normalized vector calculated above, the normalized membership value NMV of the attribute K under the network i is calculated, and the calculation method is shown in formula (14):

Further, in step three,

Step 31. Construct the decision matrix:

Synthesizing the calculation steps in step 1 and step 2, construct the speed score under different networks and the standardized membership value of each attribute, that is, the decision matrix X shown in formula (15) is obtained:

Step 32: Calculate the weight of each attribute based on AHP:

c_ii＝1，接下来计算比较矩阵的最大特征值λ_max，设其对应的特征向量为V＝(v₁,v₂,…,v_n)，将V依据式(16)归一化，则求得归一化特征向量W＝(ω₁,ω₂,…,ω_n)就是AHP求得的各属性因子的权重向量，如式(16)所示：First, construct a comparison matrix C=[c _ij ] _n×n according to the 1-9 scaling method, where c _ij represents the relative importance of attribute i to attribute j, and satisfies c _ij >0,

c _ii =1, then calculate the maximum eigenvalue λ _max of the comparison matrix, set its corresponding eigenvector as V=(v ₁ ,v ₂ ,...,v _n ), normalize V according to formula (16), Then the normalized eigenvector W=(ω ₁ ,ω ₂ ,…,ω _n ) obtained is the weight vector of each attribute factor obtained by AHP, as shown in formula (16):

Finally, the consistency test is carried out, and the CR value is calculated by formula (17). When CR<0.1, the consistency test is passed, and the weight vector W is accepted. Otherwise, the comparison matrix is re-modified. When n=3, RI=0.58,

Step 33: Calculate the comprehensive performance evaluation value of the candidate network:

After obtaining the weight vector, use Equation (18) to calculate the comprehensive performance evaluation value PEV of network i:

Step 34. Switch judgment:

Finally, the PEVs of all networks are compared. If the PEV of the current network is the largest, no handover will occur. If the PEV of the current network is not the largest, it will be switched to the candidate network with the largest PEV.

The main advantages of the present invention are as follows: the present invention proposes a vertical handover method for heterogeneous private networks based on speed pre-judgment and fuzzy logic, which can significantly reduce the number of handovers compared with the traditional RSS-based vertical handover algorithm, and has better performance.

Description of drawings

Figure 1 is a schematic diagram of a heterogeneous network system model;

Fig. 2 is the speed pre-decision curve diagram;

Fig. 3 is a membership function diagram;

Fig. 4 is a simulation scene schematic diagram;

Fig. 5 is a graph showing the variation of RSS with position;

Fig. 6 is a graph showing the variation of the average switching times with the sampling period;

Fig. 7 is a graph showing the variation of average switching times with speed;

Fig. 8 is a graph showing the variation of the average switching times with the sampling period when v=20m/s;

FIG. 9 is a graph showing the average switching times as a function of speed when T _s =2s.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The application scenario of the present invention considers a heterogeneous network composed of PDT and B-TrunC, and deploys multiple base stations of the PDT and B-TrunC networks in an area. The coverage radius of the PDT base station is much larger than that of the B-TrunC, so the latter The number of base stations is also much more than the former. Multiple macro cells (PDT) and micro cells (B-TrunC) form overlapping heterogeneous networks, and the two cells work in different frequency bands. The multimode user terminal periodically activates each network interface at a sampling interval _Ts to search for available networks, and then selects an appropriate network access point to access the network according to an effective handover decision algorithm. The heterogeneous network model is shown in Figure 1.

RSS is the most important parameter in vertical handover decision, which characterizes the power of electromagnetic waves received by the wireless terminal from the base station, that is, the received signal strength. Mobile terminals often work in complex environments such as urban buildings, so there will be a certain loss in the transmission process of electromagnetic waves in this environment. This algorithm mainly considers path transmission loss and shadow fading. Among them, the carrier frequency of B-TrunC can reach 1.5GHz, while the carrier frequency of PDT is only about 370MHz. Therefore, according to their respective frequency characteristics, Okumura-Hata and COST 231-Hata models are adopted as the path transmission loss models of PDT and B-TrunC networks, respectively.

The empirical formula for the path loss calculation of the Okumura-Hata model is as follows:

where L is the path transmission loss, the unit is dB; f _c is the electromagnetic wave operating frequency, the unit is MHz; h _b , h _re are the effective heights of the base station antenna and the receiving antenna, respectively, the unit is m; d is the base station and the mobile station The horizontal distance between them, the unit is km; α(h _re ) is the effective antenna correction factor, which is calculated by the formula (2); C _cell is the cell type correction factor, which is calculated by the formula (3).

The empirical formula for the path loss calculation of the COST 231-Hata model is as follows:

In the formula, C _M is the correction factor of the center of the big city, and the unit is dB, which is calculated by the formula (5); the meanings of the other parameters are the same as those of the formula (1).

A large number of experimental data show that shadow fading approximately obeys lognormal distribution, and its probability density function is:

In the formula, r is the local mean of the received signal, m is the expected value of r, _μs is the standard deviation, and the units of these three parameters are dB. Shadow fading follows a normal distribution with zero mean and standard deviation _μs , _which depends on the environment.

Assuming that the transmit power of the base station is P _t and the received signal strength is RSS, both units are dBm, the formula for calculating the RSS of the mobile terminal is as follows:

RSS=P _t -L+X(0, μ _s ) (7)

where X(0, μ _s ) is a Gaussian random variable representing shadow fading.

This method is a vertical handover method based on speed pre-judgment and fuzzy logic for heterogeneous private networks. Before parameter fuzzification, the unqualified network is eliminated by the speed pre-judgment module to reduce the calculation amount of subsequent handover judgment. Then use fuzzy theory to deal with some network properties with strong uncertainty, and finally consider network properties and speed scores when calculating network performance evaluation value, so that the calculation results are more reasonable and reliable. Therefore, the method is mainly divided into three parts: a speed pre-judgment processing module, a fuzzification processing module, a network performance evaluation value calculation and a handover decision module.

The present invention provides an embodiment of a method for vertical handover of heterogeneous private networks based on speed pre-judgment and fuzzy logic. The method for vertical handover of heterogeneous private networks includes the following steps:

Step 1. Speed pre-judgment: According to the different network characteristics of the PDT network and the B-TrunC network and their sensitivity to the speed of the mobile terminal, set different speed pre-judgment curve models, and obtain the network evaluation value according to the real-time speed of the terminal. Eligible networks are eliminated in advance;

Step 2. Fuzzification processing: In a qualified network, map the network parameters to three fuzzy sets of low, medium and high according to the membership function, and then normalize and quantify the membership to calculate the standardized membership value of each attribute for subsequent use. calculate;

Step 3. Calculation of network performance evaluation value: First, use the AHP to calculate the weight of each attribute, and then use the speed score value obtained in step 1 and the attribute standardized membership value obtained in step 2 to calculate the comprehensive performance evaluation of each candidate network. value, and finally select the candidate network with the largest comprehensive performance evaluation value for handover.

和

分别表示PDT和B-TrunC能够接受的终端最大移动速度，当速度大于此门限时，该网络的速度预判决得分为0，则提前将此网络从候选网络集合中删除。若速度预判决得分大于0，则保留此网络在候选网络集合中，同时将速度得分(VS)作为输入参与第三部分网络性能评估值的计算。In the preferred embodiment of this part, in step 1, in the heterogeneous network system model adopted by this algorithm, the coverage radius of PDT and B-TrunC base stations are very different, so the moving speed of the terminal will affect the network selection process. great impact. PDT is a narrowband network in a private network. The coverage of its base station can reach more than ten kilometers, which can allow the terminal to move at high speed within its coverage. The coverage of the B-TrunC base station is generally only a few kilometers. If the terminal moves too fast, good service quality cannot be guaranteed. At present, many terminals are vehicle-mounted mobile terminals, and the speed variation range is quite large. Therefore, the function of the speed pre-judgment processing module is to eliminate candidate networks that do not meet the terminal moving speed in advance, so as to reduce the calculation amount of the subsequent process and save the terminal power consumption. . The speed pre-decision functions of PDT and B-TrunC networks are shown in formulas (8) and (9), and the curve model is shown in Figure 2. where the decision threshold

and

Respectively represent the maximum moving speed of the terminal that PDT and B-TrunC can accept. When the speed is greater than this threshold, the network's speed pre-judgment score is 0, and the network is removed from the candidate network set in advance. If the speed pre-judgment score is greater than 0, the network is kept in the candidate network set, and the speed score (VS) is used as an input to participate in the calculation of the third part of the network performance evaluation value.

The speed pre-decision function of the PDT network is shown in equation (8):

The speed pre-decision function of the D-TrunC network is shown in equation (9):

和

分别表示PDT网络和B-TrunC网络能够接受的终端最大移动速度，当速度大于此门限时，该网络的速度预判决得分为0，则提前将此网络从候选网络集合中删除；若速度预判决得分大于0，则保留此网络在候选网络集合中，同时将速度得分VS作为输入参与步骤三中网络性能评估值的计算。where the decision threshold

and

Represents the maximum moving speed of the terminal that can be accepted by the PDT network and the B-TrunC network. When the speed is greater than this threshold, the network's speed pre-judgment score is 0, and the network will be deleted from the candidate network set in advance; if the speed pre-judgment is 0 If the score is greater than 0, the network is kept in the candidate network set, and the speed score VS is used as an input to participate in the calculation of the network performance evaluation value in step 3.

In the preferred embodiment of this part, in step 2, the following steps are specifically included:

Step 21. Input parameter fuzzification processing:

Referring to Figure 3, using RSS and using bandwidth B for fuzzification processing, the fuzzification processing method is: according to the membership function formulas (10), (11) and (12), the parameter value x of the attribute K is mapped to low , medium and high fuzzy sets, get membership vector

Step 22. Normalization and quantification of membership degree:

The parameter value of attribute K is normalized to three fuzzy sets of low, medium and high, and the normalized vector is obtained. The rules are shown in formula (13):

Step 23: Calculate the standardized membership value:

According to the membership degree vector and the normalized vector calculated above, the normalized membership value NMV of the attribute K under the network i is calculated, and the calculation method is shown in formula (14):

In the preferred embodiment of this part, in step 3,

Step 31. Construct the decision matrix:

Synthesizing the calculation steps in step 1 and step 2, construct the speed score under different networks and the standardized membership value of each attribute, that is, the decision matrix X shown in formula (15) is obtained:

Step 32: Calculate the weight of each attribute based on AHP:

First, construct a comparison matrix C=[c _ij ] _n×n according to the 1-9 scaling method, where c _ij represents the relative importance of attribute i to attribute j, and the value method is shown in Table 1:

Table 1

and satisfy c _ij > 0, c _ii =1, then calculate the maximum eigenvalue λ _max of the comparison matrix, set its corresponding eigenvector as V=(v ₁ ,v ₂ ,...,v _n ), normalize V according to formula (16), Then the normalized eigenvector W=(ω ₁ ,ω ₂ ,…,ω _n ) obtained is the weight vector of each attribute factor obtained by AHP, as shown in formula (16):

Finally, the consistency test is carried out, and the CR value is calculated by formula (17). When CR<0.1, the consistency test is passed, and the weight vector W is accepted. Otherwise, the comparison matrix is re-modified. When n=3, RI=0.58,

Step 33: Calculate the comprehensive performance evaluation value of the candidate network:

After obtaining the weight vector, use Equation (18) to calculate the comprehensive performance evaluation value PEV of network i:

Step 34. Switch judgment:

Finally, the PEVs of all networks are compared. If the PEV of the current network is the largest, no handover will occur. If the PEV of the current network is not the largest, it will be switched to the candidate network with the largest PEV.

A specific example is given below:

This method uses the scenario shown in Figure 4 for simulation. The unit of the coordinate axis is km. The vehicle-mounted mobile station starts from the coordinate (-12,0) and drives at a constant speed v along the positive direction of the x-axis. Two PDT base stations are set at (0,0) and (-10,0). , set up three B-TrunC base stations at (-3,0), (2,0), (7,0). Other parameter settings in the simulation process are shown in Table 2:

Table 2

The superiority of this method is verified by comparing the change of switching times with the traditional RSS-based vertical switching method.

When calculating the weight of each attribute based on the AHP, set the comparison matrix of RSS, B, VS as formula (19), and obtain the weight vector according to the steps as W=(0.546, 0.345, 0.109).

According to the above parameter settings and channel transmission model, the curve of RSS with position variation can be obtained as shown in Figure 5, which includes the signal strength curves of five base stations received at different positions.

The following focuses on comparing the difference between the traditional method based on RSS (TRSS) and this method (VPD-FL) in the index of switching times, and comparing the switching times with the sampling period T _s and the speed v respectively. To make the results more objective, all simulations were performed 50 times and then averaged.

Figure 6 shows the curve of the average switching times with the sampling period when v=10m/s. It can be seen that the average switching times of the VPD-FL method is significantly lower than that of the TRSS method, and the smaller the sampling period, the more significant the effect. With the increase of the sampling period, the average switching times of the two methods gradually decreased and finally approached the same. This is because the increase of the sampling period leads to fewer sampling points, so the number of switching decisions is also reduced, and the chance of switching is reduced accordingly.

Figure 7 is the curve of the average switching times changing with the speed when T _s =2s. Similar to Figure 6, the average switching times of the VPD-FL method is significantly lower than that of the TRSS method, and the smaller the speed, the more significant the effect. The increase in speed results in a consequent reduction in the chance of handovers, and the average number of handovers for both methods decreases gradually.

Next, the simulation comparison of whether to use the fuzzy logic algorithm of speed pre-decision is carried out. Figures 8 and 9 are a comparison of VPD-FL and fuzzy logic (FL) without speed pre-decision.

Figure 8 shows the curve of the average switching times changing with the sampling period when v=20m/s. It can be seen that the average switching times of the VPD-FL algorithm is less than that of the FL algorithm, and the smaller the sampling period, the more significant the effect. This is because the traditional FL algorithm does not consider the difference of the terminal speed between each candidate network when selecting the network, resulting in frequent switching of the terminal in the motion state among the various networks, resulting in a significant "ping-pong effect". The VPD-FL algorithm proposed by us performs pre-judgment according to the terminal speed before handover judgment, and eliminates unqualified networks in advance, so it can better solve the "ping-pong effect".

Figure 9 is the curve of the average switching times changing with the speed when T _s = 2s. The average switching times of the VPD-FL algorithm is significantly lower than that of the FL algorithm. With the increase of the speed, the average switching times of the two algorithms gradually decreases and finally approaches the same . This is because the increase in speed leads to a decrease in the chance of switching, so the average number of switching between the two algorithms will gradually decrease.

To sum up, the vertical handover method based on speed pre-decision and fuzzy logic proposed by the present invention can significantly reduce the number of handovers and have better performance compared with the traditional vertical handover method based on RSS.

Claims (4)

1. A heterogeneous private network vertical switching method based on speed pre-decision and fuzzy logic is characterized by comprising the following steps:

step one, speed pre-judgment: setting different speed pre-judging curve models according to different network characteristics of the PDT network and the B-Trunc network and the sensitivity of the PDT network and the B-Trunc network to the speed of the mobile terminal, obtaining a network evaluation value according to the real-time speed of the terminal, and removing the non-grid network in advance;

step two, fuzzification processing: in a qualified network, mapping network parameters to three fuzzy sets of low, medium and high according to a membership function, then calculating a standardized membership value of each attribute for subsequent calculation after normalization and quantization of membership;

step three, calculating a network performance evaluation value: firstly, the weight of each attribute is calculated by using an analytic hierarchy process, then the comprehensive performance evaluation value of each candidate network is calculated by using the speed score value obtained in the first step and the attribute standardized membership value obtained in the second step in a weighted mode, and finally the candidate network with the maximum comprehensive performance evaluation value is selected for switching.

2. The method as claimed in claim 1, wherein in step one, the PDT network speed pre-decision function is represented by equation (8):

the speed pre-decision function of the B-Trunc network is shown as the formula (9):

wherein the decision threshold

And

respectively representing the maximum moving speed of the terminal which can be accepted by the PDT network and the B-Trunc network, and deleting the network from the candidate network set in advance when the speed is greater than the threshold and the speed pre-judging score of the network is 0; if the speed pre-judging score is larger than 0, the network is kept in the candidate network set, and the speed score VS is used as the calculation of the network performance evaluation value in the input participation step three.

3. The method for vertical handover of a heterogeneous private network based on speed pre-decision and fuzzy logic as claimed in claim 1, wherein in the second step, the following steps are specifically included:

step two, input parameter fuzzification processing:

adopting RSS and adopting bandwidth B to perform fuzzification processing, wherein the fuzzification processing method comprises the following steps: mapping the parameter value x of the attribute K to a low, medium and high fuzzy set according to the membership function formulas (10), (11) and (12) to obtain a membership vector

Step two, normalization and quantification of membership degree:

normalizing the parameter value of the attribute K to three fuzzy sets of low, medium and high to obtain a normalized vector, wherein the rule is shown as a formula (13):

step two, calculating a standardized membership value:

and (3) calculating a standardized membership value NMV of the attribute K under the network i according to the membership vector and the normalized vector obtained above, wherein the calculation method is shown as the formula (14):

4. the method for vertical handover of heterogeneous private network based on speed pre-decision and fuzzy logic as claimed in claim 1, wherein in step three,

step three, constructing a decision matrix:

and (3) integrating the calculation steps in the first step and the second step, and constructing speed scores and standardized membership values of various attributes under different networks to obtain a decision matrix X shown as a formula (15):

step three, calculating the weight of each attribute based on an analytic hierarchy process:

firstly, a comparison matrix C ═ C is constructed according to a 1-9 scaling method_ij]_n×n，c_ijRepresents the relative importance of the attribute i to the attribute j, and satisfies c_ij＞0,

c_iiThe ratio is then calculated as 1Maximum eigenvalue λ of the comparison matrix_maxLet V be (V) as the corresponding feature vector₁,v₂,…,v_n) When V is normalized by equation (16), a normalized feature vector W is obtained as (ω)₁,ω₂,…,ω_n) The weight vector of each attribute factor obtained by AHP is as shown in equation (16):

finally, consistency check is carried out, a CR value is calculated by using the formula (17), when CR is less than 0.1, the weight vector W is accepted through the consistency check, otherwise, the comparison matrix is modified again, when n is 3, RI is 0.58,

step three, calculating the comprehensive performance evaluation value of the candidate network:

after the weight vector is obtained, the overall performance evaluation value PEV of the network i is calculated using equation (18):

step three, switching judgment:

and finally, comparing the PEVs of all the networks, if the PEV of the current network is the maximum, not switching, and if the PEV of the current network is not the maximum, switching to the candidate network with the maximum PEV.

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