CN114423063A - Heterogeneous wireless network service access control method and device based on improved gravity search algorithm - Google Patents

Heterogeneous wireless network service access control method and device based on improved gravity search algorithm Download PDF

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CN114423063A
CN114423063A CN202210060064.5A CN202210060064A CN114423063A CN 114423063 A CN114423063 A CN 114423063A CN 202210060064 A CN202210060064 A CN 202210060064A CN 114423063 A CN114423063 A CN 114423063A
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周欣欣
高志蕊
朱光伟
郭月晨
李茂源
薛青常
郭树强
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Northeast Electric Power University
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Northeast Dianli University
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Abstract

The invention provides a heterogeneous wireless network service access control method and a device based on an improved gravity search algorithm, wherein the heterogeneous wireless network service access control method specifically comprises the following steps: (1) acquiring parameters such as the rate, the network quantity, the channel bandwidth, the signal power, the noise power and the like of real-time services and non-real-time services in a heterogeneous wireless network environment; (2) establishing a heterogeneous wireless network service access control objective function by taking the maximum information transmission rate as an optimization objective; (3) solving a heterogeneous wireless network service access control objective function by using an improved gravity search algorithm; (4) and when the end condition is met, outputting an optimal solution, namely the heterogeneous wireless network service access control scheme. The heterogeneous wireless network service access control method based on the improved gravity search algorithm can ensure that more user services are accessed to a better wireless network, thereby providing higher-quality service experience for users.

Description

Heterogeneous wireless network service access control method and device based on improved gravity search algorithm
Technical Field
The invention relates to the technical field of heterogeneous wireless networks, in particular to a heterogeneous wireless network service access control method and device based on an improved gravity search algorithm.
Background
In recent years, with the rapid development of new-generation information technologies such as intelligent mobile devices, mobile internet, internet of things and the like, the number of Wireless mobile terminal users and the number of services are increased sharply, in order to meet the increasing requirements of users on network transmission rate, different operators build a plurality of base stations, different types of base stations with the same operator and the same type of base stations with different operators form different Wireless Networks, and finally, the situation that the coverage of the plurality of Wireless Networks is overlapped occurs, so that Heterogeneous Wireless Networks (HWNs) are formed. In the heterogeneous wireless network, the fusion and the advantage complementation among different networks can be performed, a plurality of internet access links can be provided for a user, and the pressure of a single network in the heterogeneous network can be relieved finally.
When different services of a user select an accessed wireless network, information such as the state of each wireless access network and user service information needs to be considered, a distribution scheme is designed and executed by a network control module, and the process is the service access control of the heterogeneous wireless network. The existing mobile terminal often cannot use a plurality of wireless networks at the same time, and only can selectively access one wireless network, so that a user needs to know states of different networks and requirements of different services on the networks to avoid problems of difficult network access, frequent network congestion and the like, therefore, how to control user services to perform optimized access in a plurality of networks according to service requirements and network capacity to improve network resource utilization rate is an important problem to be solved urgently by a heterogeneous wireless network. The invention provides a heterogeneous wireless network service access control method based on an improved gravity search algorithm, which can fully improve the utilization rate of network resources, maximize the information transmission rate, bring better network experience to users, and has easy realization of the algorithm and good selection effect.
Disclosure of Invention
In order to improve the transmission rate of network information, and to enable a user to have better network experience and improve the quality of network service for the user to preferentially access a wireless network with better condition, the invention discloses the following technical scheme:
a heterogeneous wireless network service access control method based on an improved gravity search algorithm specifically comprises the following steps:
step 1000: acquiring parameters such as the rate, the network quantity, the channel bandwidth, the signal power, the noise power and the like of real-time services and non-real-time services in a heterogeneous wireless network environment according to the state of the heterogeneous wireless network;
step 2000: constructing an objective function of heterogeneous wireless network service access control by taking the maximum information transmission rate maxr (x) as a target, wherein the optimization objective function is as follows:
Figure BDA0003477883800000021
wherein, bijRepresenting the channel bandwidth, S, to which service i is allocated in network jijRepresenting the signal power of service i in network j, NijRepresenting the noise power of the service i in the network j, and m represents the total number of the network;
step 3000: the method for solving the heterogeneous wireless network service access control objective function in the step 2000 by using the improved gravity search algorithm specifically comprises the following steps:
step 3010: randomly generating an initial population, setting the iteration times of the population and a universal gravitation constant G0Initializing the position x and the speed v of the particle;
step 3020: calculating the fitness value of each particle according to the objective function of heterogeneous wireless network service access control in the step 2000;
step 3030: and calculating the acting force of the particles j to the particles i in the population in the t iteration, wherein the calculation formula is as follows:
Figure BDA0003477883800000022
wherein the content of the first and second substances,
Figure BDA0003477883800000023
the force of particle j on particle i in the population at time t,
Figure BDA0003477883800000024
representing the position of the particle i in the D-dimensional space at time t,
Figure BDA0003477883800000025
representing the position of the particle j in the D-space, Rij(t) represents the Euclidean distance between the particles i and j at the time t, epsilon is the minimum value, the prevention denominator is 0, Maj(t) represents the active gravitational mass of the stressed individual j at time t, Mbi(t) represents the passive gravity mass of the force application individual i at the time t, G (t) is a gravity constant at the time t, and the calculation formula is as follows:
G(t)＝G0×e-αt/T (3)
wherein G is0The initial value of the constant coefficient is constantly set as 100, alpha is a descending coefficient and is constantly set as 20, T is the current iteration number, and T is the total iteration number;
step 3040: assuming M of the particleai(t)、Mbi(t)、Mii(t) and Mi(t) are equal, calculating the inertia mass of the particles, and the calculation formula is as follows:
Figure BDA0003477883800000031
wherein M isai(t) is the active gravitational mass of the stressed individual i at time t, Mbi(t) the passive gravitational mass of the forcing entity i at time t, Mii(t) represents the inertial mass of the particle i at time t, Mi(t) is the mass of particle i at a certain iteration at time t, mi(t) is the individual mass of the ith particle at time t, mj(t) is the individual mass of the jth particle, and the value range of j is [1, N]N is N particles present in the search space, fiti(t) represents the fitness value of the particle i, worst (t) represents the fitness value of the particle with the largest quality, best (t) represents the fitness value of the particle with the smallest quality, which can be respectively defined as:
Figure BDA0003477883800000032
wherein, the value range of i is [1, N ], N is N particles existing in the search space, and fit (t) represents the fitness value of the particles at the time t;
step 3050: calculating the force F of the particlei d(t) the calculation formula is:
Figure BDA0003477883800000033
wherein, Fi d(t) force of particle i in d-dimensional space at time t, randjThe value range is [0,1 ]]，
Figure BDA0003477883800000034
Representing the acting force of a particle j on a particle i in the population at the moment t, wherein D is the dimension, and N is N particles existing in a D-dimension search space;
step 3060: and (3) memorizing the self optimal information and the population optimal information of the particles by adopting the asynchronous learning factors of the formulas (7) and (8):
c1＝c1_ini+(c1_fin-c1_ini)*t/T (7)
c2＝c2_ini+(c2_fin-c2_ini)*t/T (8)
wherein, c1_ini、c2_iniRepresenting the initial learning ability, c1_fin、c2_finRepresenting the learning ability when the iteration is finished, wherein T represents the current iteration times, and T represents the maximum iteration times;
step 3070: and mapping the sine value of the particle speed to the probability value of the change of the particle position vector, wherein the calculation formula is as follows:
Figure BDA0003477883800000041
where v is the velocity value of the particle and f (v) is the probability value that maps the sine of the particle velocity to a change in the particle position vector;
step 3080: the search path of the particle is changed using equation (10):
Figure BDA0003477883800000042
wherein Levy (epsilon) is a Levy flight search path, u obeys a normal distribution curve, the value range of beta is (0,2), and u-N (0, sigma)2) V to N (0,1), σ is defined as follows:
Figure BDA0003477883800000043
wherein the value range of beta is (0,2), gamma function is represented by gamma, and the Levy (epsilon) of the Levy flight can be determined through the formulas (10) and (11);
step 3090: calculating the acceleration of the particles, wherein the acceleration of the particles i at the time t is calculated according to the following formula:
Figure BDA0003477883800000044
wherein the content of the first and second substances,
Figure BDA0003477883800000045
acceleration of particle i in d-dimensional space at time t, Mii(t) represents tInertial mass of time particle i, Fi d(t) is the acting force of the particle i in d-dimensional space at the moment t;
step 3100: and calculating the speed and position information of the particles, wherein the calculation formula is as follows:
Figure BDA0003477883800000046
randiis in the value range of [0,1 ]]，
Figure BDA0003477883800000047
Representing the velocity of the particle i at time t,
Figure BDA0003477883800000048
representing the velocity of particle i at time t +1,
Figure BDA0003477883800000049
representing the position information of the particle i at time t +1, ai(t) represents the acceleration of the particle at time t;
step 3120: if the current fitness value is good enough or the maximum iteration number is reached, the step 4000 is carried out; otherwise, adding 1 to the iteration times, and jumping to the step 3020;
step 4000: and outputting the optimal solution, namely the heterogeneous wireless network service access control scheme.
An apparatus of a heterogeneous wireless network service access control method based on an improved gravity search algorithm, the apparatus comprising:
a data acquisition module: for collecting the parameters of step 1000;
an objective function determination module: determining an objective function aiming at the user information transmission rate serving as an optimization objective in the step 2000;
a model solving module: solving an objective function by using the solving algorithm of the step 3000 aiming at the heterogeneous wireless network service access control model of the step 2000;
a scheme output module: and outputting the service access preferred scheme of the heterogeneous wireless network in the step 4000.
Compared with the prior art, the invention has the beneficial effects that:
the invention adopts the information transmission rate of the network as the objective function of the heterogeneous wireless network service access control, the information transmission rate of the network is jointly determined by all the user terminals accessed into the network, and generally, under the condition that the information transmission speed required by the user service is the same, the more the number of the services accessed into the network is, the higher the information transmission speed of the network is. The network information transmission speed can reflect the number of services accessed to the heterogeneous wireless network from the side, and the improvement of the number of the services can improve the network access decision speed and the service quality.
By adopting a sine mapping jump gravitation search algorithm based on asynchronous learning and introducing a learning mechanism, the particles can keep learning to excellent particles of the population while evolving, the memory and sharing of evolution information are kept, and the diversity of the population is improved; a sine function mapping theory is introduced, the particle speed variation is mapped into the probability of position change by using a sine function, and the convergence speed and precision are improved; introducing a Levy flight strategy, changing a particle search path, and jumping out local optima; the improved strategy effectively solves the problems that the heterogeneous wireless network service access control model is low in convergence speed and convergence precision and easy to fall into local optimum in the solving process, provides an intelligent optimization solving method for the heterogeneous wireless network service access control model, and improves the network access decision speed.
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FIG. 1 is a flow chart of a heterogeneous wireless network service access control method based on an improved gravity search algorithm;
Detailed Description
In order that the above aspects of the present invention may be more clearly understood, the present invention will now be described in further detail with reference to the accompanying drawings. It should be noted that the specific implementation described herein is only for explaining the present application and is not used to limit the present application.
Fig. 1 is a flowchart of a method for controlling service access to a heterogeneous wireless network based on an improved gravity search algorithm, specifically including the following steps:
step 1000: acquiring parameters such as the rate, the network quantity, the channel bandwidth, the signal power, the noise power and the like of real-time services and non-real-time services in a heterogeneous wireless network environment according to the state of the heterogeneous wireless network;
step 2000: with the maximum information transmission rate maxr (x) as a target, constructing an optimization objective function of heterogeneous wireless network service access control, wherein the objective function is as follows:
Figure BDA0003477883800000061
wherein, bijRepresenting the channel bandwidth, S, to which service i is allocated in network jijRepresenting the signal power of service i in network j, NijRepresenting the noise power of the service i in the network j, and m represents the total number of the network;
step 3000: the method for solving the heterogeneous wireless network service access control objective function in the step 2000 by using the improved gravity search algorithm specifically comprises the following steps:
step 3010: randomly generating an initial population, setting the iteration times of the population and a universal gravitation constant G0Initializing the position x and the speed v of the particle;
step 3020: calculating the fitness value of each particle according to the objective function of heterogeneous wireless network service access control in the step 2000;
step 3030: and calculating the acting force of the particles j to the particles i in the population in the t iteration, wherein the calculation formula is as follows:
Figure BDA0003477883800000062
wherein the content of the first and second substances,
Figure BDA0003477883800000063
the force of particle j on particle i in the population at time t,
Figure BDA0003477883800000064
representing the position of the particle i in the D-dimensional space at time t,
Figure BDA0003477883800000065
representing the position of the particle j in the D-space, Rij(t) represents the Euclidean distance between the particles i and j at the time t, epsilon is the minimum value, the prevention denominator is 0, Maj(t) represents the active gravitational mass of the stressed individual j at time t, Mbi(t) represents the passive gravity mass of the force application individual i at the time t, G (t) is a gravity constant at the time t, and the calculation formula is as follows:
G(t)＝G0×e-αt/T (3)
wherein G is0The initial value of the constant coefficient is constantly set as 100, alpha is a descending coefficient and is constantly set as 20, T is the current iteration number, and T is the total iteration number;
step 3040: assuming M of the particleai(t)、Mbi(t)、Mii(t) and Mi(t) are equal, calculating the inertia mass of the particles, and the calculation formula is as follows:
Figure BDA0003477883800000071
wherein M isai(t) is the active gravitational mass of the stressed individual i at time t, Mbi(t) the passive gravitational mass of the forcing entity i at time t, Mii(t) represents the inertial mass of the particle i at time t, Mi(t) is the mass of particle i at a certain iteration at time t, mi(t) is the individual mass of the ith particle at time t, mj(t) is the individual mass of the jth particle, and the value range of j is [1, N]N is N particles present in the search space, fiti(t) represents the fitness value of the particle i, worst (t) represents the fitness value of the particle with the largest quality, best (t) represents the fitness value of the particle with the smallest quality, which can be respectively defined as:
Figure BDA0003477883800000072
wherein, the value range of i is [1, N ], N is N particles existing in the search space, and fit (t) represents the fitness value of the particles at the time t;
step 3050: calculating the force F of the particlei d(t) the calculation formula is:
Figure BDA0003477883800000073
wherein, Fi d(t) force of particle i in d-dimensional space at time t, randjThe value range is [0,1 ]]，
Figure BDA0003477883800000074
Representing the acting force of a particle j on a particle i in the population at the moment t, wherein D is the dimension, and N is N particles existing in a D-dimension search space;
step 3060: and (3) memorizing the self optimal information and the population optimal information of the particles by adopting the asynchronous learning factors of the formulas (7) and (8):
c1＝c1_ini+(c1_fin-c1_ini)*t/T (7)
c2＝c2_ini+(c2_fin-c2_ini)*t/T (8)
wherein, c1_ini、c2_iniRepresenting the initial learning ability, c1_fin、c2_finRepresenting the learning ability when the iteration is finished, wherein T represents the current iteration times, and T represents the maximum iteration times;
step 3070: and mapping the sine value of the particle speed to the probability value of the change of the particle position vector, wherein the calculation formula is as follows:
Figure BDA0003477883800000081
where v is the velocity value of the particle and f (v) is the probability value that maps the sine of the particle velocity to a change in the particle position vector;
step 3080: the search path of the particle is changed using equation (10):
Figure BDA0003477883800000082
wherein Levy (epsilon) is a Levy flight search path, u obeys a normal distribution curve, the value range of beta is (0,2), and u-N (0, sigma)2) V to N (0,1), σ is defined as follows:
Figure BDA0003477883800000083
wherein the value range of beta is (0,2), gamma function is represented by gamma, and the Levy (epsilon) of the Levy flight can be determined through the formulas (10) and (11);
step 3090: calculating the acceleration of the particles, wherein the acceleration of the particles i at the time t is calculated according to the following formula:
Figure BDA0003477883800000084
wherein the content of the first and second substances,
Figure BDA0003477883800000085
acceleration of particle i in d-dimensional space at time t, Mii(t) represents the inertial mass of the particle i at time t, Fi d(t) is the acting force of the particle i in d-dimensional space at the moment t;
step 3100: and calculating the speed and position information of the particles, wherein the calculation formula is as follows:
Figure BDA0003477883800000091
randiis in the value range of [0,1 ]]，
Figure BDA0003477883800000092
Representing the velocity of the particle i at time t,
Figure BDA0003477883800000093
representing the velocity of particle i at time t +1,
Figure BDA0003477883800000094
representing the position information of the particle i at time t +1, ai(t) represents the acceleration of the particle at time t;
step 3120: if the current fitness value is good enough or the maximum iteration number is reached, the step 4000 is carried out; otherwise, adding 1 to the iteration times, and jumping to the step 3020;
step 4000: and outputting the optimal solution, namely the heterogeneous wireless network service access control scheme.
An apparatus of a heterogeneous wireless network service access control method based on an improved gravity search algorithm, the apparatus comprising:
a data acquisition module: for collecting the parameters of step 1000;
an objective function determination module: determining an objective function aiming at the user information transmission rate serving as an optimization objective in the step 2000;
a model solving module: solving an objective function by using the solving algorithm of the step 3000 aiming at the heterogeneous wireless network service access control model of the step 2000;
a scheme output module: and outputting the service access preferred scheme of the heterogeneous wireless network in the step 4000.
The invention discloses a heterogeneous wireless network service access control method based on an improved gravity search algorithm, solves the problems of low convergence speed and low convergence precision of a heterogeneous wireless network service access control model in the solving process, provides an intelligent optimization method for the heterogeneous wireless network service access control model, and improves the network access decision speed.
The above description is only an example of the present invention, and does not limit the scope of the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (2)

1. A heterogeneous wireless network service access control method based on an improved gravity search algorithm is characterized by comprising the following steps:
step 1000: acquiring parameters such as the rate, the network quantity, the channel bandwidth, the signal power, the noise power and the like of real-time services and non-real-time services in a heterogeneous wireless network environment according to the state of the heterogeneous wireless network;
step 2000: with the maximum information transmission rate maxr (x) as a target, constructing an optimization objective function of heterogeneous wireless network service access control, wherein the objective function is as follows:
Figure FDA0003477883790000011
wherein, bijRepresenting the channel bandwidth, S, to which service i is allocated in network jijRepresenting the signal power of service i in network j, NijRepresenting the noise power of the service i in the network j, and m represents the total number of the network;
step 3000: the method for solving the heterogeneous wireless network service access control objective function in the step 2000 by using the improved gravity search algorithm specifically comprises the following steps:
step 3010: randomly generating an initial population, setting the iteration times of the population and a universal gravitation constant G0Initializing the position x and the speed v of the particle;
step 3020: calculating the fitness value of each particle according to the objective function of heterogeneous wireless network service access control in the step 2000;
step 3030: and calculating the acting force of the particles j to the particles i in the population in the t iteration, wherein the calculation formula is as follows:
Figure FDA0003477883790000012
wherein the content of the first and second substances,
Figure FDA0003477883790000013
the force of particle j on particle i in the population at time t,
Figure FDA0003477883790000014
representing the position of the particle i in the D-dimensional space at time t,
Figure FDA0003477883790000015
representing the position of the particle j in the D-space, Rij(t) represents the Euclidean distance between the particles i and j at the time t, epsilon is the minimum value, the prevention denominator is 0, Maj(t) represents the active gravitational mass of the stressed individual j at time t, Mbi(t) represents the passive gravity mass of the force application individual i at the time t, G (t) is a gravity constant at the time t, and the calculation formula is as follows:
G(t)＝G0×e-αt/T (3)
wherein G is0The initial value of the constant coefficient is constantly set as 100, alpha is a descending coefficient and is constantly set as 20, T is the current iteration number, and T is the total iteration number;
step 3040: assuming M of the particleai(t)、Mbi(t)、Mii(t) and Mi(t) are equal, calculating the inertia mass of the particles, and the calculation formula is as follows:
Figure FDA0003477883790000021
wherein M isai(t) is the active gravitational mass of the stressed individual i at time t, Mbi(t) the passive gravitational mass of the forcing entity i at time t, Mii(t) represents the inertial mass of the particle i at time t, Mi(t) is the mass of particle i at a certain iteration at time t, mi(t) is the individual mass of the ith particle at time t, mj(t) is the individual mass of the jth particle, and the value range of j is [1, N]N is N particles present in the search space, fiti(t) represents the fitness value of the particle i, worst (t) represents the fitness value of the particle with the largest quality, best (t) represents the fitness value of the particle with the smallest quality, which can be respectively defined as:
Figure FDA0003477883790000022
wherein, the value range of i is [1, N ], N is N particles existing in the search space, and fit (t) represents the fitness value of the particles at the time t;
step 3050: calculating the force F of the particlei d(t) the calculation formula is:
Figure FDA0003477883790000023
wherein, Fi d(t) force of particle i in d-dimensional space at time t, randjThe value range is [0,1 ]]，
Figure FDA0003477883790000024
Representing the acting force of a particle j on a particle i in the population at the moment t, wherein D is the dimension, and N is N particles existing in a D-dimension search space;
step 3060: and (3) memorizing the self optimal information and the population optimal information of the particles by adopting the asynchronous learning factors of the formulas (7) and (8):
c1＝c1_ini+(c1_fin-c1_ini)*t/T (7)
c2＝c2_ini+(c2_fin-c2_ini)*t/T (8)
wherein, c1_ini、c2_iniRepresenting the initial learning ability, c1_fin、c2_finRepresenting the learning ability when the iteration is finished, wherein T represents the current iteration times, and T represents the maximum iteration times;
step 3070: and mapping the sine value of the particle speed to the probability value of the change of the particle position vector, wherein the calculation formula is as follows:
Figure FDA0003477883790000031
where v is the velocity value of the particle and f (v) is the probability value that maps the sine of the particle velocity to a change in the particle position vector;
step 3080: the search path of the particle is changed using equation (10):
Figure FDA0003477883790000032
wherein Levy (epsilon) is a Levy flight search path, u obeys a normal distribution curve, the value range of beta is (0,2), and u-N (0, sigma)2) V to N (0,1), σ is defined as follows:
Figure FDA0003477883790000033
wherein the value range of beta is (0,2), gamma function is represented by gamma, and the Levy (epsilon) of the Levy flight can be determined through the formulas (10) and (11);
step 3090: calculating the acceleration of the particles, wherein the acceleration of the particles i at the time t is calculated according to the following formula:
Figure FDA0003477883790000034
wherein the content of the first and second substances,
Figure FDA0003477883790000035
acceleration of particle i in d-dimensional space at time t, Mii(t) represents the inertial mass of the particle i at time t, Fi d(t) is the acting force of the particle i in d-dimensional space at the moment t;
step 3100: and calculating the speed and position information of the particles, wherein the calculation formula is as follows:
Figure FDA0003477883790000041
randiis in the value range of [0,1 ]]，
Figure FDA0003477883790000042
Representing the velocity of the particle i at time t,
Figure FDA0003477883790000043
representing the velocity of particle i at time t +1,
Figure FDA0003477883790000044
representing the position information of the particle i at time t +1, ai(t) represents the acceleration of the particle at time t;
step 3120: if the current fitness value is good enough or the maximum iteration number is reached, the step 4000 is carried out; otherwise, adding 1 to the iteration times, and jumping to the step 3020;
step 4000: and outputting the optimal solution, namely the heterogeneous wireless network service access control scheme.
2. An apparatus for employing the improved gravity search algorithm-based heterogeneous wireless network service access control method according to claim 1, the apparatus comprising:
a data acquisition module: for collecting the parameters of step 1000;
an objective function determination module: determining an objective function aiming at the user information transmission rate serving as an optimization objective in the step 2000;
a model solving module: solving an objective function by using the solving algorithm of the step 3000 aiming at the heterogeneous wireless network service access control model of the step 2000;
a scheme output module: and outputting the service access preferred scheme of the heterogeneous wireless network in the step 4000.
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