CN113709753B - Wireless broadband communication system site layout networking method and system - Google Patents
Wireless broadband communication system site layout networking method and system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- 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
Abstract
The application discloses a wireless broadband communication system site layout networking method and system, wherein longitude and latitude and elevation of a map are used as dimensions of a search space, an initial parent population is randomly generated, and the initial parent population which is randomly generated is set as an initial current parent population; carrying out fitness calculation on the current parent population to obtain an optimal chromosome with highest fitness, and carrying out the next step if the fitness of the optimal chromosome does not meet the condition of ending iteration; performing mutation operation on the current parent population to generate a mutation offspring population, and performing crossover operation to generate a crossover offspring population; and selecting the parent population, the variant child population and the crossed child population, selecting a plurality of chromosomes as the parent population of the next generation according to a selection operation method, ending iteration to output the optimal chromosomes if the iteration termination condition is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout. The application effectively solves the problem of complex system optimization.
Description
Technical Field
The application belongs to the technical field of communication, and particularly relates to a wireless broadband communication system site layout networking method and system.
Background
The site planning is to select the positions of the base stations on the premise of comprehensively considering parameters such as network coverage rate and the like for evaluating network performance so as to achieve the purposes of reducing the number of the required base stations as much as possible and meeting the site distribution requirement. In a wireless broadband communication system, planning and selecting a site are a key ring, and related research on the site is of high use value and is also necessary.
The site planning problem is essentially a large-scale, highly nonlinear combinatorial optimization problem, which is a typical NP-hard problem. Aiming at the more complex optimization problem in engineering. Due to a series of problems such as complexity, modeling difficulty and the like, the traditional optimization method cannot well solve the optimization problem. Heuristic-based intelligent algorithms have certain advantages in solving such problems. Genetic algorithms, a first randomized search algorithm proposed by the professor j.holland in the united states in 1975, can effectively solve this type of problem as a very representative heuristic intelligent algorithm. The genetic algorithm is an intelligent algorithm which can search the solution space to be solved directly and can automatically search the optimal solution. Because of the characteristics of strong searching capability, good convergence and the like, the genetic algorithm is often used in the optimization of actual engineering problems such as signal processing, machine learning, combination optimization and the like. The genetic algorithm is mainly used for simulating natural phenomena of inheritance, variation, selection, crossing and the like of species in the nature, and in actual operation, the phenomena are simulated by designing various genetic operators in a computer simulation manner. Aiming at the problem, the position to be solved of the base station is encoded to generate chromosomes, a plurality of random chromosomes are selected to serve as initial parent populations, child populations are generated through operations such as crossing and mutation designed based on physical characteristics of the position of the base station, fitness of each chromosome is calculated by using a fitness function aiming at map signal coverage rate, a chromosome composition population with higher fitness is selected to serve as a parent population of the next generation by using a selection operation, and the steps of the genetic algorithm are repeated until convergence condition calculation is achieved to obtain the optimal solution.
Genetic algorithms can be found to be effective in solving complex system optimization problems, particularly combinatorial optimization problems such as site planning. The core of the site planning problem of the wireless broadband communication system site is to select the best base station address to erect the base station under the condition that the specific area is within the specified established area and the longitude and latitude and elevation information of the established area are known, so that the signal coverage rate of the established area is optimal. When the genetic algorithm is used for optimization, the spatial position (including longitude, latitude and elevation) of the base station can be used as the dimension of the search space for chromosome coding, the signal coverage rate under the layout of the base station represented by each chromosome is used as the fitness to evaluate the quality of each chromosome, and the genetic algorithm is used for optimizing the optimization problem based on the settings. Through simulation results, a better result can be achieved by using a genetic algorithm and designing a proper operator.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the application is to provide a wireless broadband communication system site layout networking method and system, and the signal coverage rate is improved when the base station sites are deployed.
The application adopts the following technical scheme:
a wireless broadband communication system site layout networking method comprises the following steps:
s1, randomly generating an initial parent population by taking longitude and latitude and elevation of a map as dimensions of a search space, and setting the randomly generated initial parent population as an initial current parent population;
s2, carrying out fitness calculation on the current parent population in the step S1 to obtain an optimal chromosome with highest fitness, and if the fitness of the optimal chromosome does not meet the condition of ending iteration, carrying out the step S3;
s3, performing mutation operation on the current parent population in the step S1 to generate a mutation offspring population, and performing crossover operation to generate a crossover offspring population;
s4, selecting the parent population in the step S2, the variant offspring population obtained in the step S3 and the crossed offspring population, selecting a plurality of chromosomes as the parent population of the next generation according to a selection operation method, ending iterative output of the optimal chromosomes if the iteration termination condition in the step S2 is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
Specifically, in step S1, the random generation of the initial parent population is specifically:
the chromosomes in the initial population are randomly generated in the search space, the sampled chromosomes can be spread over the whole search space, real numbers are used for coding, the [ abscissa of the base station 1, the ordinate of the base station 1, …, the abscissa of the base station n, and the ordinate of the base station n ] are taken as the codes of the chromosomes in the population, and the initial values of the chromosomes are randomly generated.
Specifically, in step S2, if the maximum iteration number is reached or a chromosome reaching the required fitness exists in the current population, the iteration is ended to output an optimal chromosome, and according to the chromosome coding format of [ the abscissa of the base station 1, the ordinate of the base station 1, …, the abscissa of the base station n, and the ordinate of the base station n ], the optimal base station layout is decoded from the optimal chromosome, wherein [ the abscissa of the base station 1, the ordinate of the base station 1 ], …, [ the abscissa of the base station n, and the ordinate of the base station n ].
Specifically, in step S2, the fitness calculation specifically includes:
taking an area of which station setting is forbidden in the map as an obstacle area, and determining the relative position relation between the position of the base station and the obstacle area by calculating the area of a polygon formed by the position points of the base station and the vertexes of the obstacle area and the area of the obstacle area; preprocessing the site selection point, searching out a minimum rectangular area comprising each convex polygon barrier area, and judging the feasibility of the site selection point on a certain barrier area when the site selection point is positioned in the rectangular area of the barrier area;
selecting an Egli model as a channel model, and sequentially calculating the received power of all space points on the map; the method comprises the following steps: calculating the received power of a certain point on the map for all the base stations, and selecting the maximum received power as the received power of the corresponding space point; and calculating the received power of each point in the map, and when the received power reaches a threshold value, the communication system works normally to meet the signal coverage requirement of the corresponding point.
Further, the relative positional relationship between the base station position and the obstacle region is determined as follows:
point D i In a convex polygon p k In addition, if and only if
Point D i In a convex polygon p k Within, if and only if
Point D i In a convex polygon p k If and only if
Wherein,r=1, 2 for convex polygon area k ，p k Top point number of convex polygon, +.>For point D i With the edges P of polygons kr P k(r+1) The area of the triangle formed by the surrounding is->
Further, the propagation path loss of the channel model is:
L e ＝88.11+40lg(d)+20lg(f)-20lg(h t h r )
wherein L is e For propagation loss, d is the distance between the transceiver devices, h t For transmitting antenna height, h r F is the operating frequency for the height of the receiving antenna.
Specifically, in step S3, the mutation operation is performed using uniform fine mutation and strong mutation alternately.
Specifically, in step S3, the crossover operation uses two types of free crossover and dominant crossover of two optional parents, which are alternately used in the iterative process, and two chromosome vectors x 1 And x 2 The convex intersection of (2) is:
λ 1 x 1 +λ 2 x 2
wherein lambda is 1 +λ 2 ＝1,λ 1 ＞0，λ 2 ＞0。
Specifically, in step S4, the selection operation uses a relatively prohibited selection policy, specifically:
given two positive parameters α and β, during selection, when chromosome x k When selected as a next generation parent chromosome, chromosome x is prohibited from being aligned k Is selected for the neighbor chromosome of (c).
The application also provides a wireless broadband communication system site layout networking system based on a genetic algorithm, which comprises:
the generation module randomly generates an initial parent population by taking longitude and latitude and elevation of the map as dimensions of a search space, and sets the randomly generated initial parent population as an initial current parent population;
the computing module is used for computing the fitness of the current parent population of the generating module to obtain an optimal chromosome with highest fitness, and the intersecting module is used for intersecting the current parent population of the generating module if the fitness of the optimal chromosome does not meet the condition of ending iteration;
the crossing module is used for respectively carrying out mutation operation on the current parent population of the generating module to generate a mutation offspring population and carrying out crossing operation to generate a crossing offspring population;
and the networking module is used for selecting the parent population of the calculation module, the variation child population obtained by the crossing module and the crossing child population, selecting a plurality of chromosomes as the parent population of the next generation according to a method of the selection operation, ending iterative output of the optimal chromosomes if the termination iteration condition of the calculation module is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
Compared with the prior art, the application has at least the following beneficial effects:
the application discloses a wireless broadband communication system site layout networking method, and a site planning problem is essentially a large-scale and highly nonlinear combination optimization problem, and belongs to a typical NP-hard problem. Aiming at the more complex optimization problem in engineering. Due to a series of problems such as complexity, modeling difficulty and the like, the traditional optimization method cannot well solve the optimization problem. Heuristic-based intelligent algorithms have certain advantages in solving such problems. Genetic algorithms can be effective in solving complex system optimization problems, particularly combinatorial optimization problems such as site planning.
Further, in step S1, the parent population is generated randomly, so that more genetic information can be introduced, and the searching range of the whole algorithm is enlarged, so that the genetic algorithm is not premature.
Further, in step S2, if the maximum iteration count is reached or if a chromosome reaching the required fitness exists in the current population, the iteration is ended to output an optimal chromosome. The aim of the setting is to avoid invalid or less-improved iterative calculation, and improve the calculation efficiency.
Furthermore, in step S2, fitness calculation needs to be performed on each chromosome in the current population, and in this step, fitness calculation is performed on the chromosome, that is, signal coverage calculation is performed on the base station position obtained by decoding the chromosome. Chromosome fitness is a standard for screening chromosomes in a current population, and is a key step of the whole genetic algorithm.
Furthermore, the purpose of judging and setting the relative position relation between the base station position and the obstacle area is to eliminate chromosomes which do not meet the geographical requirement of the station.
Further, the purpose of the propagation path loss setting of the channel model is to calculate the received power at each point.
Further, in step S3, the mutation is performed using a uniform fine mutation and a strong mutation alternately. The two different mutation modes have the advantages that the uniform fine mutation can well retain the current optimal genetic information, so that the algorithm can perform local search near the current optimal chromosome, and the strong mutation is used for introducing more genetic information, so that the algorithm is prevented from being in premature, and the functions of retaining the optimal genetic information and preventing the algorithm from being in premature can be simultaneously achieved by combining the two mutation modes.
Further, in step S3, in the crossover operation, two kinds of free crossover and dominant crossover with optional two parents are alternately used in the iterative process. The purpose is to keep the diversity of the population and prevent the algorithm from falling into precocity.
Further, in step S4, in the selection operation, a selection policy setting that is relatively prohibited is used. The purpose is to avoid selecting chromosomes with little difference in traits, as the presence of too many chromosomes with little difference in traits in the population can cause the algorithm to fall into precocity.
In summary, the application can effectively solve the problem of complex system optimization, in particular to the problem of combined optimization such as site planning. When designing the algorithm, the real number coding is more in line with the actual situation, and the mutation operation of alternately carrying out uniform fine mutation and strong mutation, the crossover operation of alternately carrying out free crossover and dominant crossover and the selection operation of relatively forbidden are used in the algorithm, so that the algorithm is prevented from being premature, and the performance of the whole algorithm is higher.
The technical scheme of the application is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic view of a scene;
FIG. 2 is a schematic view of an obstacle region;
FIG. 3 is a schematic diagram of an obstacle region pretreatment;
fig. 4 is a flow chart of the present application.
Fig. 5 is a graph of simulation results of the algorithm proposed by the present application at different signal reception sensitivities.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
Since genetic algorithms cannot directly handle parameters of the problem space, the problem to be solved must be represented as a chromosome of the genetic space by encoding. In the present problem, the parameter of the problem space is the space coordinates of a plurality of base stations, and only the abscissas of the base stations are considered, and the elevation coordinates corresponding to the fixed abscissas are also clear because the scheme is performed on the premise of knowing map information. The chromosome design of the genetic algorithm mainly considers two forms of binary coding and real number coding, and in the problem, the coordinates of the base station are continuous real numbers, and the real number coding is used for achieving the expected effect.
The form of the real number encoded representation is: the abscissa of base station 1, the ordinate of base station 1, …, the abscissa of base station n, the ordinate of base station n.
Fitness calculations need to be performed on each chromosome in genetic algorithms to evaluate it. In this problem, the fitness is the signal coverage under the base station coordinate layout corresponding to each chromosome. Because the chromosome itself is a real number code, the three-dimensional coordinates of each base station can be directly obtained according to the chromosome coding format [ abscissa of base station 1, ordinate of base station 1, …, abscissa of base station n, ordinate of base station n ]. When the positions of a plurality of base stations are determined, the signal intensity of each point of the map can be calculated through channel modeling so as to obtain the map signal coverage rate.
Because site planning is a non-convex optimization problem, and genetic algorithm is an effective tool for solving the problem as an intelligent random search algorithm, randomness is mainly reflected in mutation and crossover operation. The mutation operation is to randomly mutate each segment of chromosome, so as to provide uncertain randomness for the algorithm. The crossover operation is to use two existing chromosomes to generate a new chromosome by a certain combination, and the operation can provide definite randomness for the algorithm on the premise of retaining prior information.
The final step of the genetic algorithm is to calculate the fitness of the parent and the variant and cross-generated offspring and select a plurality of chromosomes with higher fitness as the parent of the next generation. The selection operation essentially indicates the direction of optimization to the algorithm, corresponding to the problem, i.e. as high as possible map signal coverage.
And arranging a plurality of base stations in a known map information area, wherein each base station works in different frequency bands to provide telecommunication service for users, and the signal coverage rate of the area can be optimized by obtaining proper base station layout through algorithm calculation. A schematic view of the scene is shown in fig. 1.
Referring to fig. 4, the method for laying out and networking wireless broadband communication system sites of the present application includes the following steps:
s1, randomly generating an initial parent population by taking longitude and latitude and elevation of a map as dimensions of a search space;
the initialization of the population is realized specifically as follows:
when constructing an initial population at the initial stage of the iterative process, the distribution condition of the optimal solution is ambiguous, and no abundant population information can be utilized. In order to avoid missing any information in the search space, it is desirable that the initial population structure contains abundant information during random generation, and that excellent chromosomes are finally generated. The method employed is to randomly generate chromosomes in the initial population within the search space and require that the sampled chromosomes be spread throughout the search space. For this problem, real numbers are used, [ abscissa of base station 1, ordinate of base station 1, …, abscissa of base station n, ordinate of base station n ] are encoded versions of chromosomes in the population, the initial values of which are all randomly generated.
S2, carrying out fitness calculation on the current parent population, ending iteration output optimal chromosomes if the iteration termination condition (the maximum iteration number is reached or chromosomes which meet the requirement on fitness exist in the current population) is met, and carrying out step S3 if the iteration termination condition is not met;
the fitness calculation of each chromosome in the population is described
Compared with the traditional optimization method, the genetic algorithm has the biggest advantage that objective function gradient or higher derivative information is not needed, and only objective function values are needed to be calculated. Before calculating the objective function, it is known that some places of the map are not allowed to be selected as base stations according to actual conditions, and the area where station setting is forbidden is called an obstacle area. The obstacle region, that is, the constraint condition of the optimization target, so that the constraint condition needs to be avoided in the objective function, the feasibility of the chromosome needs to be adjusted.
S201, as can be seen from fig. 2, the relative positional relationship between the base station position and the obstacle region can be determined by calculating the area of the polygon formed by the base station position points and the obstacle region vertices and the area of the obstacle region, and the determination relationship is as follows:
point D i In a convex polygon p k In addition, if and only if
Point D i Strictly in a convex polygon p k Within, if and only if
Point D i In a convex polygon p k If and only if
In actual situations, a plurality of obstacle regions and a plurality of site selection points are present, and feasibility judgment is required. In the actual detection of feasibility, if violence makes a feasibility judgment for each obstacle area in turn for each site, such a process is often time-consuming and unnecessary. Therefore, as shown in fig. 3, the addressing point is preprocessed, the smallest rectangular area which can comprise each convex polygon obstacle area can be searched first, and the feasibility judgment of the obstacle area can be performed on the addressing point only when the addressing point is located in the rectangular area of the obstacle area.
S202, after constraint conditions in the objective function calculation are processed, the objective function calculation of the problem is described.
The channel model is selected as Egli model, and the propagation path loss is
L e ＝88.11+40lg(d)+20lg(f)-20lg(h t h r ) (4)
Wherein L is e For propagation loss (dB), d is the distance between the transceiver devices (km), h t For transmitting antenna height (m), h r For the height (m) of the receiving antenna, f is the operating frequency (MHz).
And sequentially calculating the received power of all the points to all the base stations, and selecting the maximum received power as the received power of the point. Through the analysis, the received power of each point in the map can be calculated, and when the received power reaches the threshold value, the communication system can work normally to meet the signal coverage requirement of the point.
S3, performing mutation operation on the parent population to generate a mutation offspring population, and performing crossover operation on the parent population to generate a crossover offspring population;
for crossover operations, the form of convex crossover is used, i.e. for two chromosome vectors x 1 And x 2 The convex intersection is as follows:
λ 1 x 1 +λ 2 x 2 (5)
and needs to satisfy lambda 1 +λ 2 ＝1,λ 1 ＞0，λ 2 ＞0。
The convex crossover operator actually applied depends on how parents are selected and how parents produce crossover offspring, there are usually two crossover strategies, one is free crossover of the two parents, the other is dominant crossover, i.e. the most suitable parent chromosome is used as the immobilized parent, and one chromosome is selected from the parent population as the other parent. The algorithm selects these two crossover strategies to be used alternately in the iterative process.
The mutation characteristic of the genetic algorithm can enable the solving process to randomly search the whole space where the solution possibly exists, so that the globally optimal solution can be obtained to a certain extent. Generally, variations can be classified into uniform fine variations and strong variations. Even fine variation, i.e., simply selecting a real number randomly within a specified range to replace the protogene. Let the chromosome to be mutated be x= [ x ] 1 ，x 2 ，…，x n ]Then the offspring x '= [ x ]' 1 ，x′ 2 ，…，x′ k ,…x′ n ]Wherein x' k Is x k The smaller range of the vicinity [ - ε, ε]Is a random value of the uniform distribution. The strong variation is completely random like the initial generation of chromosomes. The mutation operator of practical application selects uniform fine mutation and strong mutation to alternate.
S4, selecting the parent population, the variant offspring population and the crossed offspring population, selecting a plurality of chromosomes as the parent population of the next generation according to an algorithm of the selecting operation, and then carrying out step S2.
And (3) iterating until the iteration termination condition in the step S2 is reached, outputting an optimal chromosome, and decoding from the optimal chromosome according to the chromosome coding format of [ the abscissa of the base station 1, the ordinate of the base station 1, …, the abscissa of the base station n and the ordinate of the base station n ] to obtain an optimal base station layout of [ the abscissa of the base station 1, the ordinate of the base station 1 ], …, [ the abscissa of the base station n and the ordinate of the base station n ], wherein the base station positions are the final base station deployment positions.
The selection operation in the genetic algorithm is to determine how to select a specific partial chromosome from the parent population as the next generation parent population so that good genetic information is inherited to the next generation population. To avoid degradation during the iteration process (i.e. to fall into a locally optimal situation), a relatively forbidden selection strategy is chosen here. Chromosome x given two positive parameters α and β k The neighborhood of (c) is defined as follows:
in the selection process, once x k Selected as the next generation parent chromosome, selection of its neighbor chromosomes is prohibited. Alpha value defines x k Adaptation values, for avoiding selection of chromosomes with only small differences in fitness.
In still another embodiment of the present application, a wireless broadband communication system site layout networking system based on a genetic algorithm is provided, where the system can be used to implement the above-mentioned wireless broadband communication system site layout networking method, and in particular, the wireless broadband communication system site layout networking system based on a genetic algorithm includes a generating module, a calculating module, a crossing module and a networking module.
The generation module randomly generates an initial parent population by taking longitude and latitude and elevation of the map as dimensions of a search space, and sets the randomly generated initial parent population as an initial current parent population;
the computing module is used for computing the fitness of the current parent population of the generating module to obtain an optimal chromosome with highest fitness, and the intersecting module is used for intersecting the current parent population of the generating module if the fitness of the optimal chromosome does not meet the condition of ending iteration;
the crossing module is used for respectively carrying out mutation operation on the current parent population of the generating module to generate a mutation offspring population and carrying out crossing operation to generate a crossing offspring population;
and the networking module is used for selecting the parent population of the calculation module, the variation child population obtained by the crossing module and the crossing child population, selecting a plurality of chromosomes as the parent population of the next generation according to a method of the selection operation, ending iterative output of the optimal chromosomes if the termination iteration condition of the calculation module is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
In yet another embodiment of the present application, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor 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), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor in the embodiment of the application can be used for the operation of a wireless broadband communication system site layout networking method, which comprises the following steps:
randomly generating an initial parent population by taking longitude and latitude and elevation of a map as dimensions of a search space, and setting the randomly generated initial parent population as an initial current parent population; carrying out fitness calculation on the current parent population to obtain an optimal chromosome with highest fitness, and carrying out the next step if the fitness of the optimal chromosome does not meet the condition of ending iteration; performing mutation operation on the current parent population to generate a mutation offspring population, and performing crossover operation to generate a crossover offspring population; and selecting the parent population, the variant child population and the crossed child population, selecting a plurality of chromosomes as the parent population of the next generation according to a selection operation method, ending iteration to output the optimal chromosomes if the iteration termination condition is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
In a further embodiment of the present application, the present application also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the above-described embodiments with respect to a wireless broadband communication system site layout networking method; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:
randomly generating an initial parent population by taking longitude and latitude and elevation of a map as dimensions of a search space, and setting the randomly generated initial parent population as an initial current parent population; carrying out fitness calculation on the current parent population to obtain an optimal chromosome with highest fitness, and carrying out the next step if the fitness of the optimal chromosome does not meet the condition of ending iteration; performing mutation operation on the current parent population to generate a mutation offspring population, and performing crossover operation to generate a crossover offspring population; and selecting the parent population, the variant child population and the crossed child population, selecting a plurality of chromosomes as the parent population of the next generation according to a selection operation method, ending iteration to output the optimal chromosomes if the iteration termination condition is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The algorithm according to the application uses MATLAB R2020a for computer simulation, and the computer is configured to: the processor Intel i5-8250U 1.60GHz and the memory 8GB. The simulated environment is as follows:
table 1 simulation parameter description
The simulation results are shown in the schematic diagram 5. It can be found from the simulation result graph that the signal coverage of the map gradually decreases as the reception sensitivity increases. This result is desirable because as the reception sensitivity increases, the signal coverage radius of the base station decreases, thereby reducing the signal coverage of the map, which also verifies that the algorithm is valid from the side. In addition, it can be found that the operation time of the algorithm increases with the increase of the reception sensitivity, and the operation time of the algorithm increases because the difficulty of searching for the optimal solution increases with the increase of the reception sensitivity.
In summary, the method and system for networking site layout of wireless broadband communication system according to the present application are based on genetic algorithm, and in design, real number coding is more suitable for practical situation, and in algorithm, mutation operation of uniform fine mutation and strong mutation alternation, crossover operation of free crossover and dominant crossover alternation, and selection operation of relative prohibition are used.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above is only for illustrating the technical idea of the present application, and the protection scope of the present application is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present application falls within the protection scope of the claims of the present application.
Claims (10)
1. The wireless broadband communication system site layout networking method is characterized by comprising the following steps of:
s1, randomly generating an initial parent population by taking longitude and latitude and elevation of a map as dimensions of a search space, and setting the randomly generated initial parent population as an initial current parent population;
s2, carrying out fitness calculation on the current parent population in the step S1 to obtain an optimal chromosome with highest fitness, and if the fitness of the optimal chromosome does not meet the condition of ending iteration, carrying out the step S3, wherein the fitness calculation specifically comprises the following steps:
taking an area of which station setting is forbidden in the map as an obstacle area, and determining the relative position relation between the position of the base station and the obstacle area by calculating the area of a polygon formed by the position points of the base station and the vertexes of the obstacle area and the area of the obstacle area; preprocessing the site selection point, searching out a minimum rectangular area comprising each convex polygon barrier area, and judging the feasibility of the site selection point on a certain barrier area when the site selection point is positioned in the rectangular area of the barrier area;
selecting an Egli model as a channel model, and sequentially calculating the received power of all space points on the map; the method comprises the following steps: calculating the received power of a certain point on the map for all the base stations, and selecting the maximum received power as the received power of the corresponding space point; calculating to obtain the received power of each point in the map, and when the received power reaches a threshold value, the communication system normally works to meet the signal coverage requirement of the corresponding point;
the relative positional relationship between the base station position and the obstacle region is determined as follows:
point D i In a convex polygon p k In addition, if and only if
Point D i In a convex polygon p k Within, if and only if
Point D i In a convex polygon p k If and only if
Wherein S is Qk For convex polygon area, r=1, 2, …, p k ，p k Is the number of the top points of the convex polygon,for point D i With the edges P of polygons kr P k(r+1) The area of the triangle formed by the surrounding is->
S3, performing mutation operation on the current parent population in the step S1 to generate a mutation offspring population, and performing crossover operation to generate a crossover offspring population;
s4, selecting the parent population in the step S2, the variant offspring population obtained in the step S3 and the crossed offspring population, selecting a plurality of chromosomes as the parent population of the next generation according to a selection operation method, ending iterative output of the optimal chromosomes if the iteration termination condition in the step S2 is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
2. The method according to claim 1, wherein in step S1, the randomly generated initial parent population is specifically:
the chromosomes in the initial population are randomly generated in the search space, the sampled chromosomes can be spread over the whole search space, real numbers are used for coding, the [ abscissa of the base station 1, the ordinate of the base station 1, …, the abscissa of the base station n, and the ordinate of the base station n ] are taken as the codes of the chromosomes in the population, and the initial values of the chromosomes are randomly generated.
3. The method according to claim 1, wherein in step S2, if the maximum number of iterations is reached or if there is already a chromosome in the current population that meets the required fitness, the iteration is ended to output the optimal chromosome, and the optimal base station layout is decoded from the optimal chromosome according to the chromosome coding format of [ abscissa of base station 1, ordinate of base station 1, …, abscissa of base station n, ordinate of base station n ] to [ abscissa of base station 1, ordinate of base station 1 ], …, [ abscissa of base station n, ordinate of base station n ].
4. The method according to claim 1, wherein in step S2, the propagation path loss of the channel model is:
L e ＝88.11+40lg(d)+20lg(f)-20lg(h t h r )
wherein L is e For propagation loss, d is the distance between the transceiver devices, h t For transmitting antenna height, h r F is the operating frequency for the height of the receiving antenna.
5. The method according to claim 1, wherein in step S3, the mutation operation is performed using an alternation of uniform fine mutation and strong mutation.
6. The method according to claim 1, wherein in step S3, the crossover operation uses two types of free crossover and dominant crossover of optionally two parents, which are used alternately in an iterative process, two chromosome vectors x 1 And x 2 The convex intersection of (2) is:
λ 1 x 1 +λ 2 x 2
wherein lambda is 1 +λ 2 ＝1,λ 1 >0，λ 2 >0。
7. The method according to claim 1, characterized in that in step S4 the selection operation uses a relatively forbidden selection strategy, in particular:
given two positive parameters α and β, during selection, when chromosome x k Selected as the next generation parentChromosome x is prohibited from being aligned with chromosome x k Is selected for the neighbor chromosome of (c).
8. A wireless broadband communication system site layout networking system based on a genetic algorithm, comprising:
the generation module randomly generates an initial parent population by taking longitude and latitude and elevation of the map as dimensions of a search space, and sets the randomly generated initial parent population as an initial current parent population;
the computing module is used for computing the fitness of the current parent population of the generating module to obtain an optimal chromosome with highest fitness, and if the fitness of the optimal chromosome does not meet the condition of ending iteration, the computing module is used for intersecting, wherein the fitness computing is specifically as follows:
taking an area of which station setting is forbidden in the map as an obstacle area, and determining the relative position relation between the position of the base station and the obstacle area by calculating the area of a polygon formed by the position points of the base station and the vertexes of the obstacle area and the area of the obstacle area; preprocessing the site selection point, searching out a minimum rectangular area comprising each convex polygon barrier area, and judging the feasibility of the site selection point on a certain barrier area when the site selection point is positioned in the rectangular area of the barrier area;
selecting an Egli model as a channel model, and sequentially calculating the received power of all space points on the map; the method comprises the following steps: calculating the received power of a certain point on the map for all the base stations, and selecting the maximum received power as the received power of the corresponding space point; calculating to obtain the received power of each point in the map, and when the received power reaches a threshold value, the communication system normally works to meet the signal coverage requirement of the corresponding point;
the relative positional relationship between the base station position and the obstacle region is determined as follows:
point D i In a convex polygon p k In addition, if and only if
Point D i In a convex polygon p k Within, if and only if
Point o i In a convex polygon p k If and only if
Wherein S is Qk For convex polygon area, r=1, 2, …, p k ，p k Is the number of the top points of the convex polygon,for point D i With the edges P of polygons kr P k(r+1) The area of the triangle formed by the surrounding is->
The crossing module is used for respectively carrying out mutation operation on the current parent population of the generating module to generate a mutation offspring population and carrying out crossing operation to generate a crossing offspring population;
and the networking module is used for selecting the parent population of the calculation module, the variation child population obtained by the crossing module and the crossing child population, selecting a plurality of chromosomes as the parent population of the next generation according to a method of the selection operation, ending iterative output of the optimal chromosomes if the termination iteration condition of the calculation module is met, and decoding the optimal chromosomes to obtain the finally output optimal base station layout.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method of any of claims 1-7.
10. A computing device, comprising:
one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising steps for performing any of the methods of claims 1-7.
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