CN113784447A - 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 PDFInfo
- Publication number
- CN113784447A CN113784447A CN202111104632.9A CN202111104632A CN113784447A CN 113784447 A CN113784447 A CN 113784447A CN 202111104632 A CN202111104632 A CN 202111104632A CN 113784447 A CN113784447 A CN 113784447A
- Authority
- CN
- China
- Prior art keywords
- wireless communication
- communication network
- organism
- users
- resource allocation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004891 communication Methods 0.000 title claims abstract description 225
- 238000013468 resource allocation Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 238000013178 mathematical model Methods 0.000 claims abstract description 46
- 238000010845 search algorithm Methods 0.000 claims abstract description 40
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 58
- 230000005540 biological transmission Effects 0.000 claims description 33
- 238000004590 computer program Methods 0.000 claims description 17
- 230000003044 adaptive effect Effects 0.000 claims description 13
- 238000004519 manufacturing process Methods 0.000 claims description 9
- 230000009286 beneficial effect Effects 0.000 claims description 7
- 230000008901 benefit Effects 0.000 claims description 7
- 230000003993 interaction Effects 0.000 claims description 6
- 230000003071 parasitic effect Effects 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 5
- 240000007594 Oryza sativa Species 0.000 claims 1
- 235000007164 Oryza sativa Nutrition 0.000 claims 1
- 235000009566 rice Nutrition 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 7
- 230000006872 improvement Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009141 biological interaction Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000024241 parasitism Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a method, a device, equipment and a storage medium for optimizing wireless communication network resource allocation, wherein the method comprises the following steps: constructing a mathematical model of wireless communication network resource allocation according to the communication capacities of different user types; and taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm. The invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiont search algorithm, thereby improving the network resource utilization rate.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a method, an apparatus, a device, and a storage medium for optimizing resource allocation in a wireless communication network.
Background
The concept of the smart city is derived from the concept of a smart earth proposed by the IBM corporation in 2008, is a product of combining a digital city and the Internet of things, is considered as the direction of city development in the information era and the trend of civilized development, and essentially utilizes the modern information technology to promote the interconnection, high efficiency and intelligence of city operation systems, thereby creating a more beautiful life for people and enabling the city development to be more harmonious and more active. The concept of smart cities has attracted a great deal of attention internationally since its introduction, and has continuously brought about a booming development of smart cities around the world. The smart city becomes a strategic choice for promoting global urbanization, improving the urban treatment level, breaking the large urban diseases, improving the public service quality and developing digital economy.
With the continuous development of network strong strategy, national big data strategy, implementation of 'Internet +' action plan and digital Chinese construction, new connotation and requirements are given to cities, and the new journey of the evolution of novel smart cities is accelerated to be opened. The wireless communication network is greatly applied to the smart city, but at present, the wireless communication network resources are unreasonably distributed, so that the throughput is low, and the utilization rate of the network resources is low.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method, an apparatus, a device and a storage medium for optimizing wireless communication network resource allocation, which are used for performing intelligent optimization on wireless communication network resource allocation based on an improved symbiotic search algorithm, so as to improve the utilization rate of network resources.
In order to achieve the above object, an embodiment of the present invention provides a method for optimizing resource allocation of a wireless communication network, including:
constructing a mathematical model of wireless communication network resource allocation according to the communication capacities of different user types;
and taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
As an improvement of the above scheme, the user types include a central user, a first class user and a second class user; wherein, the central user can directly transmit data with the base station; the first class of users need other users as relay nodes, and a link is established for data transmission; the second class of users need the cooperation of users in different areas to carry out data transmission.
As an improvement of the above scheme, the building a mathematical model of resource allocation of a wireless communication network according to communication capacities of different user types specifically includes:
according to the formula
Calculating to obtain the communication capacity of the central user; wherein, PbIndicating the transmission power, P, of the base station in communicationrWhich represents the transmit power of the user at the time of communication,
n represents the number of channels of the wireless communication network, Bs represents the type of base station, and NR represents the type of user;
representing the gain of base station k and central user i on wireless communication network channel n;
representing 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 formula
Calculating to obtain the communication capacity of the users of the same type; wherein the content of the first and second substances,
representing the gain of a base station and a class of users l on a wireless communication network channel n;
representing the gain of a class of users l on a wireless communication network channel n with a central user i' as a relay node and a base station;
representing the gain of one type of user l on a wireless communication network channel n by using other types of users as relay nodes and base stations; j' represents a communication chain formed by a plurality of central users as relay nodes;
according to the formula
Calculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent the overlapping areas of the communication ranges of any two base stations in the three base stations; p is a radical ofc1Denotes the transmission power in the region c1, pc2Denotes the transmission power in the region c2, pc3Represents the transmission power in the c3 region; pc represents the sum of the transmit powers of the c1, c2, c3 regions;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c1 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c2 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c3 area;
representing the gains of the two types of users and the base station in the c1, c2 and c3 areas on the wireless communication network channel n;
indicate that the other users are utilized as the users in the two categories of the c1, c2 and c3 areasA gain of the relay node and the base station on a wireless communication network channel n;
according to the communication capacities of the central user, the first class of users and the second class of users, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is1、k2、k3Respectively representing the number of central users, first class users and second class users.
As an improvement of the above scheme, the obtaining of the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm by using the objective function of the mathematical model for allocating the wireless communication network resources as the fitness function of the improved symbiont search algorithm specifically includes:
establishing an initial organism population for an organism by using the objective function parameter of the wireless communication network resource allocation mathematical model, and initializing the parameter of the initial organism population;
taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest；
Randomly selecting two different organisms U from the initial population of organismsiAnd UjSearch in mutually beneficial stages according to formula
Production of novel organisms
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; wherein MV is UiAnd UjThe interaction vector of (a) is calculated,
rand (0, 1) is a vector of random numbers, bf1And bf2Is a benefit factor;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming a Lei stage search, introducing a Lei-V flight strategy and adaptive weights to generate new organisms
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;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming parasitic phase search according to formula
Upv(pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Generation of variant organism UpvUpdating the initial organism population according to the relationship between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the variation bit, ub and lb represent the upper and lower bounds of the search space, respectively;
judging whether the maximum iteration times is reached, if so, outputting an optimal solution, wherein the optimal solution is the optimal allocation of the wireless communication network resources; if not, returning to the step, taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest。
As an improvement of the scheme, the parameters of the initial organism population comprise a population size N, a maximum iteration number T and upper and lower limits of a search space.
As an improvement of the above scheme, 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 to obtain an original organism U according to the fitness functioni、UjAnd a novel organism
Is a fitness value fi、fj、
For original organism UiIf, if
Then receive new organism
If it is not
Then the original organism U is retainedi；
For organism UjIf, if
Then receive new organism
If it is not
Then the original organism U is retainedj。
As an improvement of the scheme, the introduction of the Laevir flight strategy and the adaptive weight generation of new organisms
The method specifically comprises the following steps:
introducing a Levy flight strategy lexy-u-t-λλ is more than 1 and less than or equal to 3, and the updating formula of the Levy flight strategy step length is as follows:
wherein the parameter beta is 1.5,
and
representing the gamma function, the variance of the parameters is:
σv＝1；
introducing adaptive weights
Wherein L is the current iteration frequency, and L is the maximum iteration frequency;
according to the formula
Production of novel organisms
The embodiment of the invention also provides a device for optimizing the resource allocation of the wireless communication network, which comprises the following steps:
the building module is used for building a wireless communication network resource allocation mathematical model according to the communication capacities of different user types;
and the optimization module is used for taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
The embodiment of the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements the method for optimizing wireless communication network resource allocation described in any of the above.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the foregoing methods for optimizing wireless communication network resource allocation.
Compared with the prior art, the method, the device, the equipment and the storage medium for optimizing the resource allocation of the wireless communication network provided by the embodiment of the invention have the beneficial effects that: constructing a mathematical model of wireless communication network resource allocation according to communication capacities of different user types; and taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm. The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiont search 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 resource allocation of a wireless communication network according to the present invention;
FIG. 2 is a diagram of a wireless communication network resource allocation model in a preferred embodiment of a method for optimizing wireless communication network resource allocation provided in the present invention;
fig. 3 is a schematic structural diagram of a preferred embodiment of a wireless communication network resource allocation optimizing apparatus provided in the present invention;
fig. 4 is a schematic structural diagram of a preferred embodiment of a terminal device provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for optimizing resource allocation of a wireless communication network according to a preferred embodiment of the present invention. The method for optimizing the resource allocation of the wireless communication network comprises the following steps:
s1, constructing a mathematical model of wireless communication network resource allocation according to the communication capacity of different user types;
s2, taking the objective function of the wireless communication network resource allocation mathematical model as the fitness function of the improved symbiont search algorithm, and obtaining the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
In another preferred embodiment, the user types include a central user, a first type of user, and a second type of user; wherein, the central user can directly transmit data with the base station; the first class of users need other users as relay nodes, and a link is established for data transmission; the second class of users need the cooperation of users in different areas to carry out data transmission.
In another preferred embodiment, the S1, constructing a mathematical model of resource allocation of a wireless communication network according to communication capacities of different user types, specifically includes:
s101, according to the formula
Calculating to obtain the communication capacity of the central user; wherein, PbIndicating the transmission power, P, of the base station in communicationrWhich represents the transmit power of the user at the time of communication,
n denotes the number of channels of the wireless communication network, Bs denotes the base station type, and NR denotesA user type;
representing the gain of base station k and central user i on wireless communication network channel n;
representing 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 formula
Calculating to obtain the communication capacity of the users of the same type; wherein the content of the first and second substances,
representing the gain of a base station and a class of users l on a wireless communication network channel n;
representing the gain of a class of users l on a wireless communication network channel n with a central user i' as a relay node and a base station;
representing the gain of one type of user l on a wireless communication network channel n by using other types of users as relay nodes and base stations; j' represents a communication chain formed by a plurality of central users as relay nodes;
s103, according to the formula
Calculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent the overlapping areas of the communication ranges of any two base stations in the three base stations; p is a radical ofc1Denotes the transmission power in the region c1, pc2Denotes the transmission power in the region c2, pc3Represents the transmission power in the c3 region; pc represents the sum of the transmit powers of the c1, c2, c3 regions;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c1 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c2 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c3 area;
representing the gains of the two types of users and the base station in the c1, c2 and c3 areas on the wireless communication network channel n;
representing the gains of the two types of users in the c1, c2 and c3 areas on the wireless communication network channel n by using other users as relay nodes and the base station;
s104, according to the communication capacities of the central user, the first class user and the second class user, constructing a mathematical model of the wireless communication network resource allocation, wherein the mathematical model comprises the following steps:
wherein k is1、k2、k3Respectively representing the number of central users, first class users and second class 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 method for optimizing wireless communication network resource allocation according to the present invention. A wireless communication network may be generally divided into a plurality of areas, each area having a different number of users and different types of users, with different users having different communication rights. In a wireless communication network, each user can communicate with a base station independently, or can realize communication with the base station by forwarding data cooperatively among users, when the number of current users is large, different users can occupy the same communication resource at the same moment, so that part of the resources are heavier in load, and other parts of the resources are idle for a long time, and the problem of unbalanced resource allocation occurs. In this embodiment, a mathematical model of resource allocation of a wireless communication network is constructed according to communication capacities of different user types, where the user types include a central user, a first class user, and a second class user. Wherein, one class of users is the R class of users in fig. 2, and the second class of users is the C class of users 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, PbIndicating the transmission power, P, of the base station in communicationrWhich represents the transmit power of the user at the time of communication,
n represents the number of channels of the wireless communication network, Bs represents the type of base station, and NR represents the type of user;
representing the gain of base station k and central user i on wireless communication network channel n;
representing 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 users, other users (other type of users and central users) are required to be used as relay nodes, and a link is established for data transmission.
According to the formula
Calculating to obtain the communication capacity of one type of users; wherein the content of the first and second substances,
representing the gain of a base station and a class of users l on a wireless communication network channel n;
representing the gain of a class of users l on a wireless communication network channel n with a central user i' as a relay node and a base station;
representing the gain of one type of user l on a wireless communication network channel n by using other types of users as relay nodes and base stations; j' represents a communication chain formed by a plurality of central users as relay nodes;
for the second type of users, users in different areas (a central user, a first type of users or a second type of users) are required to cooperate to perform data transmission. According to the formula
Calculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent the overlapping areas of the communication ranges of any two base stations in the three base stations; p is a radical ofc1Denotes the transmission power in the region c1, pc2Denotes the transmission power in the region c2, pc3Represents the transmission power in the c3 region; p is a radical ofcRepresents the sum of the transmission power of the c1, c2 and c3 regions;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c1 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c2 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c3 area;
representing the gains of the two types of users and the base station in the c1, c2 and c3 areas on the wireless communication network channel n;
representing the gains of the two types of users in the c1, c2 and c3 areas on the wireless communication network channel n by using other users as relay nodes and the base station;
in order to improve the communication quality of the wireless communication network, the wireless communication network resource analysis method adopts the maximum system transmission rate as an objective function, and constructs a wireless communication network resource allocation mathematical model according to the communication capacities of a central user, a first class user and a second class user as follows:
wherein k is1、k2、k3Respectively representing the number of central users, first class users and second class users.
In another preferred embodiment, the step S2, taking the objective function of the mathematical model of wireless communication network resource allocation as a fitness function of an improved symbiont search algorithm, and obtaining the optimal allocation of the wireless communication network resource through the improved symbiont search algorithm specifically includes:
s201, establishing an initial organism population for an organism by using an objective function parameter of the wireless communication network resource allocation mathematical model, and initializing the parameter of the initial organism population;
s202, taking the objective function of the wireless communication network resource allocation mathematical model as the fitness function of the improved symbiont search algorithm, and calculating the fitness functionObtaining the fitness value of each organism in the initial organism population to obtain the optimal organism U in the initial organism populationbest；
S203, randomly selecting two different organisms U from the initial organism populationiAnd UjSearch in mutually beneficial stages according to formula
Production of novel organisms
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; wherein MV is UiAnd UjThe interaction vector of (a) is calculated,
rand (0, 1) is a vector of random numbers, bf1And bf2Is a benefit factor;
s204, randomly selecting two different organisms U from the initial organism populationiAnd UjPerforming a Lei stage search, introducing a Lei-V flight strategy and adaptive weights to generate new organisms
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;
s205, randomly selecting two different organisms U from the initial organism populationiAnd UjPerforming parasitic phase search according to formula
Upv(pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Generation of variant organism UpvUpdating the initial organism population according to the relationship between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the variation bit, ub and lb represent the upper and lower bounds of the search space, respectively;
s206, judging whether the maximum iteration times is reached, if so, outputting an optimal solution, wherein the optimal solution is the optimal allocation of the wireless communication network resources; if not, returning to the step, taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest。
Preferably, the parameters of the initial organism population include a population size N, a maximum iteration number T, and upper and lower bounds of a search space.
Preferably, 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 to obtain an original organism U according to the fitness functioni、UjAnd a novel organism
Mean fitness value fi、fj、
For original organism UiIf, if
Then receive new organism
If it is not
Then the original organism U is retainedi；
For organism UjIf, if
Then receive new organism
If it is not
Then the original organism U is retainedj。
Preferably, the introduction of the Levis flight strategy and the adaptive weight generation of the new organism
The method specifically comprises the following steps:
introducing a Levy flight strategy lexy-u-t-λλ is more than 1 and less than or equal to 3, and the updating formula of the Levy flight strategy step length is as follows:
wherein the parameter beta is 1.5,
and
representing the gamma function, the variance of the parameters is:
σv＝1；
introducing adaptive weights
Wherein L is the current iteration frequency, and L is the maximum iteration frequency;
according to the formula
To generate new tissueBiological body
Specifically, the symbiotic search algorithm simulates the interaction behavior among individuals observed in nature, and the biological interaction process is divided into three stages of mutual benefit, fragment benefit and parasitism. Symbiotic theory describes the relationship between two organisms, one of which is beneficial and the other is not significantly harmed or helped.
In this embodiment, an initial organism population is constructed for an organism by using an objective function parameter of a wireless communication network resource allocation mathematical model, and for the parameter of the initial organism population: initializing the population size N, the maximum iteration number T and the upper and lower bounds of the search space. Assigning resources of a wireless communication network to an objective function of a mathematical model, i.e.
Calculating the fitness value of each organism in the initial organism population as a fitness function of an improved symbiont search algorithm, sequencing the fitness values of all the organisms, and taking the organism with the maximum fitness value as the optimal organism U in the initial organism populationbest。
Random selection of two different organisms U from an initial population of organismsiAnd UjSearch in mutually beneficial stages according to formula
Production of novel organisms
Wherein MV is UiAnd UjThe interaction vector of (a) is calculated,
rand (0, 1) is a vector of random numbers, bf1And bf2Is a benefit factor;
calculating according to the fitness function to obtain the original organism Ui、UjAnd a novel organism
Fitness value of
fi、fj、
For original organism UiIf, if
Then receive new organism
If it is not
Then the original organism U is retainedi(ii) a For organism UjIf, if
Then receive new organism
If it is not
Then the original organism U is retainedj。
Random selection of two different organisms U from an initial population of organismsiAnd UjThe Lei-Ri stage search is carried out, and a Lei-Wei flight strategy lexy-u-t is introduced-λ，1＜λ≤3，
The update formula of the Levis flight strategy step length is as follows:
wherein the parameter beta is 1.5,
and
representing the gamma function, the variance of the parameters is:
σv＝1；
introducing adaptive weights
Wherein L is the current iteration frequency, and L is the maximum iteration frequency;
according to the formula
Production of novel organisms
Calculating according to the fitness function to obtain the original organism UiAnd a novel organism
Is a fitness value fi、
If it is not
Then receive new organism
If it is not
Then the original organism U is retainedi。
Random selection of two different organisms U from an initial population of organismsiAnd UjPerforming parasitic phase search according to formula
Upv(pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Generation of variant organism UpvWherein pick represents the variation bit, ub and lb represent the upper and lower bounds of the search space, respectively;
calculating according to the fitness function to obtain the original organism UiAnd variant organism UpvIs a fitness value fi、fpvE.g. fpv＞fiThen receive the variant organism Upv(ii) a If f ispv＜fiThen the original organism U is retainedi。
Judging whether the maximum iteration times is reached, if so, outputting an optimal solution, wherein the optimal solution is the optimal allocation of wireless communication network resources; if not, returning to the step, taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of the improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest。
The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiont search algorithm, thereby improving the network resource utilization rate.
Correspondingly, the invention also provides a device for optimizing the wireless communication network resource allocation, which can realize all the processes of the method for optimizing the wireless communication network resource allocation in the embodiment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a wireless communication network resource allocation optimizing apparatus according to a preferred embodiment of the present invention. The wireless communication network resource allocation optimizing device comprises:
a building module 301, configured to build a mathematical model of resource allocation of a wireless communication network according to communication capacities of different user types;
an optimizing module 302, configured to use an objective function of the mathematical model for allocating wireless communication network resources as a fitness function of an improved symbiont search algorithm, and obtain optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
Preferably, the user types include a central user, a first type of user and a second type of user; wherein, the central user can directly transmit data with the base station; the first class of users need other users as relay nodes, and a link is established for data transmission; the second class of users need the cooperation of users in different areas to carry out data transmission.
Preferably, the building block 301 is specifically configured to:
according to the formula
Calculating to obtain the communication capacity of the central user; wherein, PbIndicating the transmission power, P, of the base station in communicationrWhich represents the transmit power of the user at the time of communication,
n represents the number of channels of the wireless communication network, Bs represents the type of base station, and NR represents the type of user;
representing the gain of base station k and central user i on wireless communication network channel n;
representing 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 formula
Calculating to obtain the communication capacity of the users of the same type; wherein the content of the first and second substances,
indicating that a base station and a class of users l are in wireless communicationGain on network channel n;
representing the gain of a class of users l on a wireless communication network channel n with a central user i' as a relay node and a base station;
representing the gain of one type of user l on a wireless communication network channel n by using other types of users as relay nodes and base stations; j' represents a communication chain formed by a plurality of central users as relay nodes;
according to the formula
Calculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent the overlapping areas of the communication ranges of any two base stations in the three base stations; p is a radical ofc1Denotes the transmission power in the region c1, pc2Denotes the transmission power in the region c2, pc3Represents the transmission power in the c3 region; pc represents the sum of the transmit powers of the c1, c2, c3 regions;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c1 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c2 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c3 area;
representing the gains of the two types of users and the base station in the c1, c2 and c3 areas on the wireless communication network channel n;
representing the gains of the two types of users in the c1, c2 and c3 areas on the wireless communication network channel n by using other users as relay nodes and the base station;
according to the communication capacities of the central user, the first class of users and the second class of users, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is1、k2、k3Respectively representing the number of central users, first class users and second class users.
Preferably, the optimization module 302 is specifically configured to:
establishing an initial organism population for an organism by using the objective function parameter of the wireless communication network resource allocation mathematical model, and initializing the parameter of the initial organism population;
taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest；
Randomly selecting two different organisms U from the initial population of organismsiAnd UjSearch in mutually beneficial stages according to formula
Production of novel organisms
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; wherein MV is UiAnd UjThe interaction vector of (a) is calculated,
rand (0, 1) is a vector of random numbers, bf1And bf2Is a benefit factor;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming a Lei stage search, introducing a Lei-V flight strategy and adaptive weights to generate new organisms
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;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming parasitic phase search according to formula
Upv(pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Generation of variant organism UpvUpdating the initial organism population according to the relationship between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the variation bit, ub and lb represent the upper and lower bounds of the search space, respectively;
judging whether the maximum iteration times is reached, if so, outputting an optimal solution, wherein the optimal solution is the optimal allocation of the wireless communication network resources; if not, returning to the step, taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest。
Preferably, the parameters of the initial organism population include a population size N, a maximum number of iterations T, and upper and lower search space bounds.
Preferably, 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 to obtain an original organism U according to the fitness functioni、UjAnd a novel organism
Is a fitness value fi、fj、
For original organism UiIf, if
Then receive new organism
If it is not
Then the original organism U is retainedi；
For organism UjIf, if
Then receive new organism
If it is not
Then the original organism U is retainedj。
Preferably, the introducing the lavi flight strategy and the adaptive weight generate new organisms
The method specifically comprises the following steps:
introducing a Levy flight strategy lexy-u-t-λλ is more than 1 and less than or equal to 3, and the updating formula of the Levy flight strategy step length is as follows:
wherein the parameter beta is 1.5,
and
representing the gamma function, the variance of the parameters is:
σv＝1；
introducing adaptive weights
Wherein L is the current iteration frequency, and L is the maximum iteration frequency;
according to the formula
Production of novel organisms
In a specific implementation, the working principle, the control flow and the technical effect of the wireless communication network resource allocation optimization apparatus provided in the embodiment of the present invention are the same as those of the wireless communication network resource allocation optimization method in the foregoing embodiment, and are not described herein again.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a terminal device according to a preferred embodiment of 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 allocation of resources of a 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.) that are stored in the memory 402 and executed by the processor 401 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor 401 may be any conventional Processor, the Processor 401 is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.
The memory 402 mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory 402 may be a high speed random access memory, a non-volatile memory such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or the memory 402 may be other volatile solid state memory devices.
It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the structural diagram of fig. 4 is only an example of the terminal device and does not constitute a limitation of the terminal device, and may include more or less components than those shown, or combine some components, or different components.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the method for optimizing wireless communication network resource allocation according to any of the above embodiments.
The embodiment of the invention provides a method, a device, equipment and a storage medium for optimizing wireless communication network resource allocation, wherein a mathematical model for the wireless communication network resource allocation is constructed according to communication capacities of different user types; and taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm. The embodiment of the invention carries out intelligent optimization on the wireless communication network resource allocation based on the improved symbiont search algorithm, thereby improving the network resource utilization rate.
It should be noted that the above-described system embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the system provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for optimizing resource allocation of a wireless communication network, comprising:
constructing a mathematical model of wireless communication network resource allocation according to the communication capacities of different user types;
and taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
2. The method of claim 1, wherein the user types include a center user, a class one user, and a class two user; wherein, the central user can directly transmit data with the base station; the first class of users need other users as relay nodes, and a link is established for data transmission; the second class of users need the cooperation of users in different areas to carry out data transmission.
3. The method for optimizing resource allocation in a wireless communication network according to claim 2, wherein the constructing a mathematical model of resource allocation in a wireless communication network according to communication capacities of different user types comprises:
according to the formula
Calculating to obtain the communication capacity of the central user; wherein, PbIndicating the transmission power, P, of the base station in communicationrWhich represents the transmit power of the user at the time of communication,
n represents the number of channels of the wireless communication network, Bs represents the type of base station, and NR represents the type of user;
representing the gain of base station k and central user i on wireless communication network channel n;
representing 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 formula
Calculating to obtain the communication capacity of the users of the same type; wherein the content of the first and second substances,
representing the gain of a base station and a class of users l on a wireless communication network channel n;
representing the gain of a class of users l on a wireless communication network channel n with a central user i' as a relay node and a base station;
representing the gain of one type of user l on a wireless communication network channel n by using other types of users as relay nodes and base stations; j' represents a communication chain formed by a plurality of central users as relay nodes;
according to the formula
Calculating to obtain the communication capacity of the second class of users; wherein c1, c2 and c3 respectively represent the overlapping areas of the communication ranges of any two base stations in the three base stations; p is a radical ofc1Denotes the transmission power in the region c1, pc2Denotes the transmission power in the region c2, pc3Represents the transmission power in the c3 region; pc represents the sum of the transmit powers of the c1, c2, c3 regions;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c1 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c2 area;
represents the gain of the second class of users and the base station on the wireless communication network channel n in the c3 area;
representing the gains of the two types of users and the base station in the c1, c2 and c3 areas on the wireless communication network channel n;
representing the gains of the two types of users in the c1, c2 and c3 areas on the wireless communication network channel n by using other users as relay nodes and the base station;
according to the communication capacities of the central user, the first class of users and the second class of users, constructing a mathematical model of the wireless communication network resource allocation as follows:
wherein k is1、k2、k3Respectively representing the number of central users, first class users and second class users.
4. The method for optimizing the allocation of resources to a wireless communication network according to claim 1, wherein the obtaining the optimal allocation of resources to the wireless communication network through the improved symbiont search algorithm by using the objective function of the mathematical model for allocating resources to a wireless communication network as a fitness function of the improved symbiont search algorithm specifically comprises:
establishing an initial organism population for an organism by using the objective function parameter of the wireless communication network resource allocation mathematical model, and initializing the parameter of the initial organism population;
taking the objective function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest；
Randomly selecting two different organisms U from the initial population of organismsiAnd UjSearch in mutually beneficial stages according to formula
Production of novel organisms
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; wherein MV is UiAnd UjThe interaction vector of (a) is calculated,
rand (0, 1) is a vector of random numbers, bf1And bf2Is a benefit factor;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming a Lei stage search, introducing a Lei-V flight strategy and adaptive weights to generate new organisms
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;
randomly selecting two different organisms U from the initial population of organismsiAnd UjPerforming parasitic phase search according to formula
Upv(pick)＝rand(1，length(pick))*(ub(pick)-lb(pick))+lb(pick)
Generation of variant organism UpvUpdating the initial organism population according to the relationship between the fitness value of the original organism and the fitness value of the variant organism; wherein pick represents the variation bit, ub and lb represent the upper and lower bounds of the search space, respectively;
judging whether the maximum iteration times is reached, if so, outputting an optimal solution, wherein the optimal solution is the optimal allocation of the wireless communication network resources; if not, returning to the step, taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm, calculating the fitness value of each organism in the initial organism population, and obtaining the optimal organism U in the initial organism populationbest。
5. The method of claim 4, wherein the parameters of the initial population of organisms include a population size N, a maximum number of iterations T, and upper and lower search space bounds.
6. The method of claim 4, wherein the updating the initial population of organisms according to the relationship between the fitness value of the original organism and the fitness value of the new organism comprises:
calculating to obtain an original organism U according to the fitness functioni、UjAnd a novel organism
Is a fitness value fi、fj、
For original organism UiIf, if
Then receive new organism
If it is not
Then the original organism U is retainedi；
For organism UjIf, if
Then receive new organism
If it is not
Then the original organism U is retainedj。
7. The method of claim 4, wherein the introducing the Rice flight strategy and the adaptive weight generation new organism
The method specifically comprises the following steps:
introducing a Levy flight strategy lexy-u-t-λλ is more than 1 and less than or equal to 3, and the updating formula of the Levy flight strategy step length is as follows:
wherein the parameter beta is 1.5,
and
representing the gamma function, the variance of the parameters is:
introducing adaptive weights
Wherein L is the current iteration frequency, and L is the maximum iteration frequency;
according to the formula
Production of novel organisms
8. An apparatus for optimizing resource allocation in a wireless communication network, comprising:
the building module is used for building a wireless communication network resource allocation mathematical model according to the communication capacities of different user types;
and the optimization module is used for taking the target function of the wireless communication network resource allocation mathematical model as a fitness function of an improved symbiont search algorithm and acquiring the optimal allocation of the wireless communication network resources through the improved symbiont search algorithm.
9. 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 of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for optimizing allocation of resources in a wireless communication network according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111104632.9A CN113784447B (en) | 2021-09-22 | 2021-09-22 | Method, device, equipment and storage medium for optimizing wireless communication network resource allocation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111104632.9A CN113784447B (en) | 2021-09-22 | 2021-09-22 | Method, device, equipment and storage medium for optimizing wireless communication network resource allocation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113784447A true CN113784447A (en) | 2021-12-10 |
| CN113784447B CN113784447B (en) | 2023-11-17 |
Family
ID=78852502
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111104632.9A Active CN113784447B (en) | 2021-09-22 | 2021-09-22 | Method, device, equipment and storage medium for optimizing wireless communication network resource allocation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113784447B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2274943A1 (en) * | 2008-05-09 | 2011-01-19 | Telefonaktiebolaget L M Ericsson (PUBL) | Resource allocation in uplink ofdma |
| CN108901074A (en) * | 2018-07-23 | 2018-11-27 | 华东交通大学 | A kind of mobile subscriber's frequency spectrum distributing method based on cuckoo searching algorithm |
-
2021
- 2021-09-22 CN CN202111104632.9A patent/CN113784447B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2274943A1 (en) * | 2008-05-09 | 2011-01-19 | Telefonaktiebolaget L M Ericsson (PUBL) | Resource allocation in uplink ofdma |
| CN108901074A (en) * | 2018-07-23 | 2018-11-27 | 华东交通大学 | A kind of mobile subscriber's frequency spectrum distributing method based on cuckoo searching algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 李中捷;谢东朋;: "异构蜂窝网络中联合功率控制的终端直通通信资源分配", 计算机应用, no. 09 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113784447B (en) | 2023-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109840154B (en) | Task dependency-based computing migration method in mobile cloud environment | |
| Goudarzi et al. | A fast hybrid multi-site computation offloading for mobile cloud computing | |
| Gao et al. | A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems | |
| Truong et al. | Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem | |
| Cui et al. | A novel offloading scheduling method for mobile application in mobile edge computing | |
| CN107766135A (en) | Method for allocating tasks based on population and simulated annealing optimization in mobile cloudlet | |
| CN111586720A (en) | Task unloading and resource allocation combined optimization method in multi-cell scene | |
| Tam et al. | A decomposition-based multi-objective optimization approach for balancing the energy consumption of wireless sensor networks | |
| CN108762927B (en) | Multi-target task scheduling method for mobile cloud computing | |
| CN105869053A (en) | Two-stage memetic based social network influence maximizing method | |
| CN112784362A (en) | Hybrid optimization method and system for unmanned aerial vehicle-assisted edge calculation | |
| CN109743713B (en) | Resource allocation method and device for electric power Internet of things system | |
| CN111324422A (en) | Multi-target virtual machine deployment method, device, equipment and storage medium | |
| CN111553834B (en) | Concurrent graph data preprocessing method based on FPGA | |
| CN114567933A (en) | Resource allocation method in heterogeneous cloud and mist cooperative network based on improved genetic algorithm | |
| CN115543556A (en) | Adaptive symbolic regression method based on multitask genetic programming algorithm | |
| CN110719335B (en) | Resource scheduling method, system and storage medium under space-based cloud computing architecture | |
| CN116185523A (en) | Task unloading and deployment method | |
| CN111885493A (en) | Micro-cloud deployment method based on improved cuckoo search algorithm | |
| Du et al. | A genetic simulated annealing algorithm to optimize the small-world network generating process | |
| CN114884908A (en) | Data synchronization method, device, equipment and storage medium | |
| CN113784447A (en) | Method, device, equipment and storage medium for optimizing wireless communication network resource allocation | |
| CN108200623B (en) | Centralized path calculation and energy saving method based on genetic algorithm | |
| Din et al. | A genetic algorithm for solving dual-homing cell assignment problem of the two-level wireless ATM network | |
| CN108260193B (en) | Joint resource allocation method and device based on channel aggregation in heterogeneous network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |