CN113784447B - Method, device, equipment and storage medium for optimizing wireless communication network resource allocation - Google Patents

Method, device, equipment and storage medium for optimizing wireless communication network resource allocation Download PDF

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CN113784447B
CN113784447B CN202111104632.9A CN202111104632A CN113784447B CN 113784447 B CN113784447 B CN 113784447B CN 202111104632 A CN202111104632 A CN 202111104632A CN 113784447 B CN113784447 B CN 113784447B
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wireless communication
communication network
users
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CN113784447A (en
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林凡
郭淑林
刘晨阳
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GCI Science and Technology Co Ltd
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a method, a device, equipment and a storage medium for optimizing wireless communication network resource allocation, wherein the wireless communication network resource allocation optimization comprises the following steps: constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types; and taking the objective function of the mathematical model of the wireless communication network resource allocation as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resource through the improved symbiotic searching algorithm. The invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiotic searching algorithm, thereby improving the network resource utilization rate.

Description

Method, device, equipment and storage medium for optimizing wireless communication network resource allocation
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for optimizing wireless communications network resource allocation.
Background
The concept of the smart city is derived from the concept of the smart earth proposed by IBM corporation in 2008, is a product of combining a digital city with the internet of things, is considered as the direction of urban development in the information age, and is the trend of civilization development, and the essence of the concept is that the modern information technology is used for promoting the interconnection, the high efficiency and the intelligence of urban operation systems, so that better life is created for people, and the urban development is more harmonious and more active. The concept of smart cities has been internationally attracting a great deal of attention since its proposal, and has continuously triggered the development of global smart cities. Smart cities have become strategic choices for advancing global urbanization, improving urban governance levels, breaking large urban diseases, improving public quality of service, and developing digital economy.
With the implementation of the network strong national strategy, the national big data strategy and the Internet plus action plan and the continuous development of digital Chinese construction, the city is endowed with new connotation and requirements, and the new sign of the evolution of the novel smart city is accelerated to be opened. Wireless communication networks are greatly applied in smart cities, but at present, wireless communication network resources are unreasonably distributed, so that the throughput is low, and the network resource utilization rate is low.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a wireless communication network resource allocation optimization method, device, equipment and storage medium, which are used for intelligently optimizing the wireless communication network resource allocation based on an improved symbiotic searching algorithm so as to improve the network resource utilization rate.
In order to achieve the above object, an embodiment of the present invention provides a method for optimizing wireless communication network resource allocation, including:
constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types;
and taking the objective function of the mathematical model of the wireless communication network resource allocation as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resource through the improved symbiotic searching algorithm.
As an improvement of the above scheme, the user types include a central user, a class of users and a class of users; wherein, the central user can directly perform data transmission with the base station; the user needs other users as relay nodes, and establishes a link for data transmission; and the second class of users need to perform data transmission in cooperation with users in different areas.
As an improvement of the above solution, the constructing a mathematical model of wireless communication network resource allocation according to communication capacities of different user types specifically includes:
according to the formulaCalculating to obtain the communication capacity of the central user; wherein P is b Indicating the transmit power of the base station at the time of communication,P r representing the transmit power of the user during communication, +.>N represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; />Representing the gains of base station k and central user i on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node;
according to the formulaCalculating to obtain the communication capacity of the user; wherein (1)>Representing the gains of the base station and a class of users l on the wireless communication network channel n; / >Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; />Representing the gain of one class of users/on a wireless communication network channel n by using other class of users as relay nodes and base stations; j' represents a communication link formed by a plurality of central users acting as relay nodes;
according to the formulaCalculating to obtain the communication capacity of the second class of users; wherein c1, c2, and c3 each represent three groupsOverlapping areas of communication ranges of any two base stations in the station; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; pc represents the sum of the transmission powers of the c1, c2 and c3 areas; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 3; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
According to the communication capacity of the central user, the first class user and the second class user, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
As an improvement of the above solution, the obtaining the optimal allocation of the wireless communication network resources by using the improved symbiotic search algorithm with the objective function of the mathematical model of the wireless communication network resource allocation as the fitness function of the improved symbiotic search algorithm specifically includes:
constructing an initial organism population by using the objective function parameters of the wireless communication network resource allocation mathematical model, and initializing the parameters of the initial organism population;
taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best ；
Randomly selecting two different organisms U from said initial organism population i And U j Performing a reciprocal phase search according to the formula
Generating new-born objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism; wherein MV is U i And U j Is (are) interaction vector>rand (0, 1) is a vector of random numbers, bf 1 And bf 2 Is a benefit factor;
randomly selecting two different organisms U from said initial organism population i And U j Performing slice-benefit stage search, introducing a Lewy flight strategy and self-adaptive weight to generate newBiological bodyUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism;
randomly selecting two different organisms U from said initial organism population i And U j Performing parasitic phase search according to the formula
U pv (pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Producing variant organism U pv Updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the metamorphism, ub and lb represent the upper and lower bounds of the search space, respectively;
judging whether the maximum iteration times are reached, if so, outputting an optimal solution, namely, optimal allocation of the wireless communication network resources; if not, returning to the step of taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function for improving the symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best 。
As an improvement of the above scheme, the parameters of the initial organism population include population size N, maximum iteration number T, and search space upper and lower bounds.
As an improvement of the above-mentioned aspect, the updating the initial organism population according to the relationship between the fitness value of the original organism and the fitness value of the new organism specifically includes:
calculating according to the fitness function to obtain a protoplasm U i 、U j And new-born objectsThe fitness value f of (2) i 、f j 、/>
For protoplasm U i If (3)Then accept new born->If->Then the original object U is reserved i ；
For living body U j If (3)Then accept new born->If->Then the original object U is reserved j 。
As an improvement of the scheme, the Laiweighing strategy and the adaptive weight are introduced to generate new objectsThe method specifically comprises the following steps:
the Lewy flight strategy lexy-u=t is introduced -λ The updating formula of the Laiwei flight strategy step length is that lambda is more than 1 and less than or equal to 3:wherein the parameter β=1.5, +.>And->Representing gamma functions, parametersThe variance of the numbers is: />σ v ＝1；
Introducing adaptive weightsWherein L is the current iteration number, and L is the maximum iteration number;
according to the formulaProducing new-born objects->
The embodiment of the invention also provides a wireless communication network resource allocation optimizing device, which comprises:
The construction module is used for constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types;
and the optimization module is used for taking the objective function of the wireless communication network resource allocation mathematical model as the fitness function of the improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiotic searching algorithm.
The embodiment of the invention also provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the wireless communication network resource allocation optimization method according to any one of the above when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the wireless communication network resource allocation optimization method.
Compared with the prior art, the method, the device, the equipment and the storage medium for optimizing the wireless communication network resource allocation have the beneficial effects that: constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types; and taking the objective function of the mathematical model of the wireless communication network resource allocation as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resource through the improved symbiotic searching algorithm. The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiotic searching algorithm, thereby improving the network resource utilization rate.
Drawings
Fig. 1 is a schematic flow chart of a preferred embodiment of a method for optimizing wireless communication network resource allocation according to the present invention;
fig. 2 is a diagram of a wireless communication network resource allocation model in a preferred embodiment of a wireless communication network resource allocation optimization method provided by the present invention;
fig. 3 is a schematic structural diagram of a preferred embodiment of a wireless communication network resource allocation optimizing device according to the present invention;
fig. 4 is a schematic structural diagram of a preferred embodiment of a terminal device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a preferred embodiment of a method for optimizing wireless communication network resource allocation according to the present invention. The wireless communication network resource allocation optimization method comprises the following steps:
S1, constructing a mathematical model of wireless communication network resource allocation according to communication capacities of different user types;
s2, taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic searching algorithm, and obtaining the optimal allocation of the wireless communication network resources through the improved symbiotic searching algorithm.
In another preferred embodiment, the user types include a center user, a class of users, and a class of users; wherein, the central user can directly perform data transmission with the base station; the user needs other users as relay nodes, and establishes a link for data transmission; and the second class of users need to perform data transmission in cooperation with users in different areas.
In another preferred embodiment, the step S1 of constructing a mathematical model of wireless communication network resource allocation according to communication capacities of different user types specifically includes:
s101, according to the formulaCalculating to obtain the communication capacity of the central user; wherein P is b Representing the transmission power of a base station during communication, P r Indicating the transmission power of the user at the time of communication,n represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; / >Representing the gains of base station k and central user i on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node;
s102, according to the formulaCalculating to obtain the communication capacity of the user; wherein (1)>Representing the gains of the base station and a class of users l on the wireless communication network channel n; />Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; />Representing the gain of one class of users/on a wireless communication network channel n by using other class of users as relay nodes and base stations; j' represents a communication link formed by a plurality of central users acting as relay nodes;
s103, according to the formulaCalculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent overlapping areas of communication ranges of any two base stations in the three base stations; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; pc represents the sum of the transmission powers of the c1, c2 and c3 areas; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; / >Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Indicating that the class II users and base stations in the c3 region are in the wireless communication network channel nGain on; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
s104, constructing a mathematical model of the wireless communication network resource allocation according to the communication capacities of the central user, the first class user and the second class user, wherein the mathematical model comprises the following steps:
wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
Specifically, referring to fig. 2, fig. 2 is a diagram of a wireless communication network resource allocation model in a preferred embodiment of a wireless communication network resource allocation optimization method according to the present invention. A wireless communication network may be generally divided into a plurality of areas, each having a different number of users and different types of users, the different users having different communication rights. In a wireless communication network, each user can communicate with a base station independently, and can also realize communication with the base station by cooperatively forwarding data between users, when the number of the current users is large, different users probably occupy the same communication resource at the same moment, so that part of resources are heavy in load, other part of resources are idle for a long time, and the problem of unbalanced resource allocation occurs. In this embodiment, a mathematical model of wireless communication network resource allocation is constructed according to communication capacities of different user types, where the user types include a central user, a class of users, and a class of users. Wherein, one type of user is the R type user in fig. 2, and the second type of user is the C type user in fig. 2.
For the central user, the data transmission can be directly carried out with the base station. According to the formula
Calculating to obtain the communication capacity of the central user; wherein P is b Representing the transmission power of a base station during communication, P r Indicating the transmission power of the user at the time of communication,n represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; />Representing the gains of base station k and central user i on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node;
for one type of user, other users (other types of users and central users) are required to serve as relay nodes, and a link is established for data transmission.
According to the formulaCalculating to obtain the communication capacity of a class of users; wherein (1)>Representing the gains of the base station and a class of users l on the wireless communication network channel n; />Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; />Representing the gain of one class of users/on a wireless communication network channel n by using other class of users as relay nodes and base stations; j' represents a communication link formed by a plurality of central users acting as relay nodes;
For the second-class users, users (central users, first-class users or second-class users) in different areas are required to cooperate for data transmission. According to the formula
Calculating to obtain the communication capacity of the second class users; wherein c1, c2 and c3 respectively represent overlapping areas of communication ranges of any two base stations in the three base stations; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; p is p c The sum of the transmission powers in the areas c1, c2 and c3 is shown;representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 3; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
in order to make the communication quality of the wireless communication network higher, the wireless communication network resource analysis method adopts the maximized system transmission rate as an objective function, and constructs a mathematical model of wireless communication network resource allocation according to the communication capacities of the central user, the first class user and the second class user as follows:
Wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
In yet another preferred embodiment, the step S2 of using the objective function of the mathematical model of the wireless communication network resource allocation as an fitness function of an improved symbiotic search algorithm, and obtaining the optimal allocation of the wireless communication network resource by the improved symbiotic search algorithm specifically includes:
s201, constructing an initial organism population for organisms by using objective function parameters of the wireless communication network resource allocation mathematical model, and initializing the parameters of the initial organism population;
s202, taking an objective function of the wireless communication network resource allocation mathematical model as an adaptability function for improving a symbiotic organism searching algorithm, and calculating an adaptability value of each organism in the initial organism population to obtain an optimal organism U in the initial organism population best ；
S203, randomly selecting two different organisms U from the initial organism population i And U j Performing a reciprocal phase search according to the formula
Generating new-born objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism; wherein MV is U i And U j Is (are) interaction vector>rand (0, 1) is a vector of random numbers, bf 1 And bf 2 Is a benefit factor;
s204, randomly selecting two different organisms U from the initial organism population i And U j Performing a slice-benefit phase search, introducing a Lewy flight strategy and adaptive weights to generate new objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism;
s205, randomly selecting two different organisms U from the initial organism population i And U j Performing parasitic phase search according to the formula
U pv (pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Producing variant organism U pv Updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the metamorphism, ub and lb represent the upper and lower bounds of the search space, respectively;
s206, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, namely, the optimal allocation of the wireless communication network resources; if not, returning to the step of taking the objective function of the mathematical model of the wireless communication network resource allocation as the objective functionImproving the fitness function of the symbiotic organism searching algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism population best 。
Preferably, the parameters of the initial organism population include population size N, maximum iteration number T, and search space upper and lower bounds.
Preferably, the updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism specifically includes:
calculating according to the fitness function to obtain a protoplasm U i 、U j And new-born objectsAll fitness value f i 、f j 、/>
For protoplasm U i If (3)Then accept new born->If->Then the original object U is reserved i ；
For living body U j If (3)Then accept new born->If->Then the original object U is reserved j 。
Preferably, the method comprises the steps of introducing a Laiwo flight strategy and adaptive weights to generate new objectsThe method specifically comprises the following steps:
the Lewy flight strategy lexy-u=t is introduced -λ The updating formula of the Laiwei flight strategy step length is that lambda is more than 1 and less than or equal to 3:wherein the parameter β=1.5, +.>And->Representing gamma functions, the variance of the parameters is: />σ v ＝1；
Introducing adaptive weightsWherein L is the current iteration number, and L is the maximum iteration number;
according to the formulaProducing new-born objects->
Specifically, the symbiotic biological search algorithm simulates interaction behaviors among individuals observed in the natural world, and the biological interaction process is divided into three stages of reciprocity, sheet benefit and parasitism. Symbiotic theory describes the relationship between two organisms, one that benefits without the other being significantly injured or helped.
In the embodiment, an initial organism population is constructed by organisms according to objective function parameters of a mathematical model of wireless communication network resource allocation, and parameters of the initial organism population are compared with the parameters of the initial organism population: and initializing the population scale N, the maximum iteration number T and the upper and lower boundaries of the search space. Objective function of mathematical model for allocating wireless communication network resources, i.eAs the fitness function of the improved symbiotic searching algorithm, calculating the fitness value of each organism in the initial organism population, sequencing the fitness values of all organisms, and taking the organism with the largest fitness value as the optimal organism U in the initial organism population best 。
Randomly selecting two different organisms U from an initial organism population i And U j Performing a reciprocal phase search according to the formula
Generating new-born objectsWherein MV is U i And U j Is (are) interaction vector>rand (0, 1) is a vector of random numbers, bf 1 And bf 2 Is a benefit factor;
calculating according to the fitness function to obtain the original object U i 、U j And new-born objectsIs adapted to the degree of adaptation value of (a)
f i 、f j 、For protoplasm U i If->Then accept new born->If->Then the original object U is reserved i The method comprises the steps of carrying out a first treatment on the surface of the For living body U j If->Then accept new born->If->Then the original object U is reserved j 。
Randomly selecting two different organisms U from an initial organism population i And U j Performing a slice interest stage search, and introducing a Lewy flight strategy lexy-u=t -λ ，1＜λ≤3，
The updating formula of the Laiwei flight strategy step length is as follows:
wherein, the parameter beta=1.5,and->Representing gamma functions, the variance of the parameters is: />σ v ＝1；
Introducing adaptive weightsWherein L is the current iteration number, and L is the maximum iteration number;
according to the formulaProducing new-born objects->
Calculating according to the fitness function to obtain the original object U i And new-born objectsThe fitness value f of (2) i 、/>If it isThen accept new born->If->Then the original object U is reserved i 。
Randomly selecting two different organisms U from an initial organism population i And U j Performing parasitic phase search according to the formula
U pv (pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Producing variant organism U pv Wherein pick represents the variant bit, ub and lb represent the upper and lower bounds of the search space, respectively;
according to adaptationCalculating the degree function to obtain the original object U i Variant organisms U pv The fitness value f of (2) i 、f pv For example f pv ＞f i Then receive variant organism U pv The method comprises the steps of carrying out a first treatment on the surface of the If f pv ＜f i Then reserve the original object U i 。
Judging whether the maximum iteration times are reached, if so, outputting an optimal solution, namely, optimal allocation of wireless communication network resources; if not, returning to the step of taking the objective function of the wireless communication network resource allocation mathematical model as the fitness function for improving the symbiotic biological search algorithm, and calculating the fitness value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best 。
The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiotic searching algorithm, thereby improving the network resource utilization rate.
Correspondingly, the invention also provides a wireless communication network resource allocation optimizing device which can realize all the flows of the wireless communication network resource allocation optimizing method in the embodiment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a wireless communication network resource allocation optimization device according to a preferred embodiment of the present invention. The wireless communication network resource allocation optimizing device comprises:
a construction module 301, configured to construct a mathematical model of wireless communication network resource allocation according to communication capacities of different user types;
an optimization module 302, configured to take an objective function of the mathematical model of wireless communication network resource allocation as an fitness function of an improved symbiotic search algorithm, and obtain an optimal allocation of the wireless communication network resource through the improved symbiotic search algorithm.
Preferably, the user types include a center user, a class of users and a class of users; wherein, the central user can directly perform data transmission with the base station; the user needs other users as relay nodes, and establishes a link for data transmission; and the second class of users need to perform data transmission in cooperation with users in different areas.
Preferably, the construction module 301 is specifically configured to:
according to the formulaCalculating to obtain the communication capacity of the central user; wherein P is b Representing the transmission power of a base station during communication, P r Indicating the transmission power of the user at the time of communication,n represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; />Representing the gains of base station k and central user i on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node;
according to the formulaCalculating to obtain the communication capacity of the user; wherein (1)>Representing the gains of the base station and a class of users l on the wireless communication network channel n; />Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; />Representing a class of usersl using other users as the gains of the relay node and the base station on a wireless communication network channel n; j' represents a communication link formed by a plurality of central users acting as relay nodes;
according to the formulaCalculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent overlapping areas of communication ranges of any two base stations in the three base stations; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; pc represents the sum of the transmission powers of the c1, c2 and c3 areas; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 3; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
according to the communication capacity of the central user, the first class user and the second class user, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
Preferably, the optimizing module 302 is specifically configured to:
constructing an initial organism population by using the objective function parameters of the wireless communication network resource allocation mathematical model, and initializing the parameters of the initial organism population;
Taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best ；
Randomly selecting two different organisms U from said initial organism population i And U j Performing a reciprocal phase search according to the formula
/>
Generating new-born objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism; wherein MV is U i And U j Is (are) interaction vector>rand (0, 1) is a vector of random numbers, bf 1 And bf 2 To advantage(s)A benefit factor;
randomly selecting two different organisms U from said initial organism population i And U j Performing a slice-benefit phase search, introducing a Lewy flight strategy and adaptive weights to generate new objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism;
randomly selecting two different organisms U from said initial organism population i And U j Performing parasitic phase search according to the formula
U pv (pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Producing variant organism U pv Updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the metamorphism, ub and lb represent the upper and lower bounds of the search space, respectively;
judging whether the maximum iteration times are reached, if so, outputting an optimal solution, namely, optimal allocation of the wireless communication network resources; if not, returning to the step of taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function for improving the symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best 。
Preferably, the parameters of the initial organism population include population size N, maximum number of iterations T, and search space upper and lower bounds.
Preferably, the updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism specifically includes:
calculating according to the fitness function to obtain a protoplasm U i 、U j And new-born objectsThe fitness value f of (2) i 、f j 、/>
For protoplasm U i If (3)Then accept new born- >If->Then the original object U is reserved i ；
For living body U j If (3)Then accept new born->If->Then the original object U is reserved j 。
Preferably, the introducing of the Lewy flight strategy and adaptive weights generates new objectsThe method specifically comprises the following steps:
the Lewy flight strategy lexy-u=t is introduced -λ The updating formula of the Laiwei flight strategy step length is that lambda is more than 1 and less than or equal to 3:wherein the parameter β=1.5, +.>And->Representing gamma functions, the variance of the parameters is: />σ v ＝1；
Introducing adaptive weightsWherein L is the current iteration number, and L is the maximum iteration number; />
According to the formulaProducing new-born objects->
In a specific implementation, the working principle, control flow and technical effects of the wireless communication network resource allocation optimization device provided by the embodiment of the present invention are the same as those of the wireless communication network resource allocation optimization method in the above embodiment, and are not repeated here.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a preferred embodiment of a terminal device according to the present invention. The terminal device comprises a processor 401, a memory 402 and a computer program stored in the memory 402 and configured to be executed by the processor 401, wherein the processor 401 implements the method for optimizing the allocation of resources of the wireless communication network according to any of the above embodiments when executing the computer program.
Preferably, the computer program may be divided into one or more modules/units (e.g. computer program 1, computer program 2, etc.) which are stored in the memory 402 and executed by the processor 401 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
The processor 401 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which may be microprocessors, or the processor 401 may be any conventional processor, the processor 401 being a control center of the terminal device, with various interfaces and lines connecting the various parts of the terminal device.
The memory 402 mainly includes a program storage area, which may store an operating system, an application program required for at least one function, and the like, and a data storage area, which may store related data and the like. In addition, the memory 402 may be a high-speed random access memory, a nonvolatile memory such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), etc., or the memory 402 may be other volatile solid-state memory devices.
It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor, a memory, and those skilled in the art will understand that the schematic structural diagram of fig. 4 is merely an example of the above-mentioned terminal device, and does not limit the above-mentioned terminal device, and may include more or fewer components than those shown, or may combine some components or different components.
The embodiment of the invention also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, the equipment where the computer readable storage medium is located is controlled to execute the wireless communication network resource allocation optimization method according to any embodiment.
The embodiment of the invention provides a wireless communication network resource allocation optimization method, a device, equipment and a storage medium, wherein a mathematical model of wireless communication network resource allocation is constructed according to the communication capacity of different user types; and taking the objective function of the mathematical model of the wireless communication network resource allocation as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resource through the improved symbiotic searching algorithm. The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiotic searching algorithm, thereby improving the network resource utilization rate.
It should be noted that the system embodiments described above are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the system embodiment of the present invention, the connection relationship between the modules represents that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims (8)

1. A method for optimizing allocation of resources in a wireless communication network, comprising:
constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types;
taking an objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiotic searching algorithm;
the user types comprise center users, one type of users and two types of users; the central user can directly perform data transmission with the base station; the user needs other users as relay nodes, and establishes a link for data transmission; the second class of users need to perform data transmission through user cooperation in different areas;
the construction of a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types specifically comprises the following steps:
According to the formulaCalculating to obtain the communication capacity of the central user; wherein P is b Representing the transmission power of a base station during communication, P r Representing the transmit power of the user during communication, +.>N represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; />Representing the gains of base station k and central user u on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node; r represents the number of communication links;
according to the formulaCalculating to obtain the communication capacity of the user; wherein (1)>Indicating the gain of a communication link j formed by a base station and a class of users l on a wireless communication network channel n; />Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; />Representing the gain of one class of users/on a wireless communication network channel n by using other class of users as relay nodes and base stations; j' represents a communication link formed by a plurality of central users acting as relay nodes;
according to the formulaCalculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent overlapping areas of communication ranges of any two base stations in the three base stations; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; p is p c The sum of the transmission powers in the areas c1, c2 and c3 is shown; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 3; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
according to the communication capacity of the central user, the first class user and the second class user, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
2. The method for optimizing allocation of wireless communication network resources according to claim 1, wherein the obtaining the optimal allocation of the wireless communication network resources by using the modified symbiont search algorithm by using the objective function of the mathematical model of the wireless communication network resource allocation as an fitness function of the modified symbiont search algorithm specifically comprises:
Constructing an initial organism population by using the objective function parameters of the wireless communication network resource allocation mathematical model, and initializing the parameters of the initial organism population;
taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best ；
Randomly selecting two different organisms U from said initial organism population i And U j Performing a reciprocal phase search according to the formula
Generating new-born objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism; wherein MV is U i And U j Is (are) interaction vector>rand (0, 1) is a vector of random numbers, bf 1 And bf 2 Is a benefit factor;
randomly selecting two different organisms U from said initial organism population i And U j Performing a slice-benefit phase search, introducing a Lewy flight strategy and adaptive weights to generate new objectsUpdating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the new organism;
Randomly selecting two different organisms U from said initial organism population i And U j Performing parasitic phase search according to the formula
U pv (pick)＝rand(1,length(pick))*(ub(pick)-lb(pick))+lb(pick)
Producing variant organism U pv Updating the initial organism population according to the relation between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents a variantUb and lb represent the upper and lower bounds, respectively, of the search space;
judging whether the maximum iteration times are reached, if so, outputting an optimal solution, namely, optimal allocation of the wireless communication network resources; if not, returning to the step of taking the objective function of the wireless communication network resource allocation mathematical model as an adaptability function for improving the symbiotic biological search algorithm, and calculating the adaptability value of each organism in the initial organism population to obtain the optimal organism U in the initial organism population best 。
3. The method of claim 2, wherein the parameters of the initial organism population include population size, maximum number of iterations, and search space upper and lower bounds.
4. The method for optimizing wireless communication network resource allocation according to claim 2, wherein updating the initial organism population according to a relationship between the fitness value of the original organism and the fitness value of the new organism, specifically comprises:
Calculating according to the fitness function to obtain a protoplasm U i 、U j And new-born objectsThe fitness value f of (2) i 、f j 、f i new 、/>
For protoplasm U i If f i new >f i Then receive the new objectIf f i new <f i Then reserve the original object U i ；
For protoplasm U j If (3)Then accept new born->If->Then the original object U is reserved j 。
5. The method of optimizing wireless communication network resource allocation of claim 2, wherein the introducing of the lewy flight strategy and adaptive weights generates new organismsThe method specifically comprises the following steps:
the Lewy flight strategy lexy-u=t is introduced -λ ,1<The lambda is less than or equal to 3, and the updating formula of the Laiwei flight strategy step length is as follows:wherein the parameter β=1.5, +.>And->Γ represents gamma function, the variance of the parameter is:σ v ＝1；
introducing adaptive weightsWherein L is the current iteration number, and L is the maximum iteration number;
according to the formulaProducing new-born objects->
6. A wireless communication network resource allocation optimization device, comprising:
the construction module is used for constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types;
the optimization module is used for taking an objective function of the wireless communication network resource allocation mathematical model as an adaptability function of an improved symbiotic searching algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiotic searching algorithm;
The user types comprise center users, one type of users and two types of users; the central user can directly perform data transmission with the base station; the user needs other users as relay nodes, and establishes a link for data transmission; the second class of users need to perform data transmission through user cooperation in different areas;
the construction module is specifically configured to:
according to the formulaCalculating to obtain the communication capacity of the central user; wherein P is b Representing the transmission power of a base station during communication, P r Representing the transmit power of the user during communication, +.>N represents the number of channels of the wireless communication network, bs represents the base station type, and NR represents the user type; />Representing the gains of base station k and central user i on wireless communication network channel n; />Indicating the gain of a communication link j formed by a base station k and a plurality of central users on a wireless communication network channel n; i' represents a central user as a relay node; r represents the number of communication links;
according to the formulaCalculating to obtain the communication capacity of the user; wherein (1)>Indicating the gain of a communication link j formed by a base station and a class of users l on a wireless communication network channel n; />Representing the gain of a class of users i on a wireless communication network channel n by using a central user i' as a relay node and a base station; / >Representing the gain of one class of users/on a wireless communication network channel n by using other class of users as relay nodes and base stations; j' represents a communication link formed by a plurality of central users acting as relay nodes;
according to the formulaCalculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent overlapping areas of communication ranges of any two base stations in the three base stations; p is p c1 Representing the transmit power in the c1 region, p c2 Representing the transmit power in the c2 region, p c3 Representing the transmit power in the c3 region; p is p c Representing the sum of the transmission powers in the c1, c2 and c3 regions；/>Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 1; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 2; />Representing the gains of the class II users and the base stations on a wireless communication network channel n in the region c 3; />Representing gains of the class II users and the base stations in the areas c1, c2 and c3 on a wireless communication network channel n; />The gains of the second class users in the areas c1, c2 and c3 on the wireless communication network channel n of the relay node and the base station are represented by other users;
according to the communication capacity of the central user, the first class user and the second class user, constructing a mathematical model of the wireless communication network resource allocation as follows:
Wherein k is 1 、k 2 、k 3 Respectively representing the number of central users, one type of users and two types of users.
7. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the wireless communication network resource allocation optimization method according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the wireless communication network resource allocation optimization method according to any one of claims 1 to 5.
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