Station placement design method, station placement design program, station placement design device, and wireless communication system construction method

Abstract

Provided are a station placement design method, a station placement design program, a station placement design device, and a wireless communication system construction method that enable station placement design so as to satisfy a plurality of different communication demands. In the station placement design method, a generation change for renewing a plurality of individuals of a population is performed using a method of a multi-objective genetic algorithm on the basis of an evaluation using a plurality of objective functions for each individual of the population. Each individual has genetic information representing whether or not a wireless base station 4 is installed at each installation candidate point 13 disposed in a target area. Subsequently, Pareto optimal solutions are extracted as a solution set from the population after the generation change has been performed a plurality of times. Subsequently, by selecting one individual from the solution set with higher priority given to improving the evaluation value by an objective function having higher priority, a station placement design pattern corresponding to the individual is obtained. The plurality of objective functions include an objective function calculated on the basis of the overall wireless communication quality of a wireless terminal 2 in the target area when the wireless base station 4 is installed at the installation candidate point 13.

Description

Site design method, site design program, site design device, and method for constructing a wireless communication system

[0001] This disclosure relates to a site design method, a site design program, a site design device, and a method for constructing a wireless communication system.

[0002] There is a limit to the range of radio waves that can reach from a wireless base station. Furthermore, there is a limit to the number of wireless terminals that a single wireless base station can accommodate. Therefore, if the number of wireless base stations is insufficient, problems arise such as inadequate coverage and insufficient capacity for wireless terminals. On the other hand, if the number of wireless base stations is excessive, it becomes inefficient due to increased costs for the base station equipment itself. For these reasons, when determining the placement of wireless base stations, it is necessary to place a sufficient number of base stations in appropriate locations, and various base station placement design methods, such as those disclosed in Non-Patent Document 1, are being considered.

[0003] Furthermore, site placement is a combinatorial optimization problem, and various methods have been proposed for solving general combinatorial optimization problems. For example, the evolutionary algorithm shown in Non-Patent Document 2 is proposed as a population-based metaheuristic optimization algorithm inspired by evolutionary mechanisms such as reproduction, mutation, genetic modification, natural selection, and survival of the fittest. Also, the multi-objective genetic algorithm shown in Non-Patent Document 3 is proposed as a method for application to multi-objective optimization problems. In multi-objective optimization problems, when each objective function is in a trade-off relationship, it is difficult to obtain a single optimal solution, so the concept of a Pareto optimal solution has been introduced instead of the concept of an optimal solution.

[0004] Toshiro Nakahira, Daisuke Murayama, Satoshi Takaya, Kenichi Kawamura, Takayoshi Moriyama, "Multi-Wireless Area Design Method Based on Communication Capacity and Base Station Cost," IEICE Technical Review, IEICE General Convention, B-5-97, Mar. 2022. Shota Yagami, Sho Kuwajima, "Evolutionary Algorithms," [online], April 2014, [Retrieved September 6, 2024], Internet <URL: http: / / mikilab.doshisha.ac.jp / dia / monthly / monthly2014 / mlm152 / syagami / syagami.pdf> Tomoyuki Hiroyasu, Mitsunori Miki, Shinya Watanabe, Takeshi Sakoda, Jiro Uemura, "Comparison of Various Methods in Multi-Objective Genetic Algorithms," Doshisha University Journal of Science and Engineering, Vol. 43, No. 1, pp. 41-52, April 2002.

[0005] When designing the placement of wireless base stations, it is sometimes necessary to simultaneously satisfy multiple different communication needs within the target area. For example, the placement design method disclosed in Non-Patent Document 1 places wireless base stations one by one from multiple candidate placement locations using a greedy method, but it does not take into account the simultaneous consideration of different communication needs when placing wireless base stations. As a result, it was not possible to design a placement that satisfies multiple different communication needs.

[0006] This disclosure relates to solving such problems. This disclosure provides a site design method, a site design program, and a site design device that enable site design to meet multiple different communication needs, as well as a method for constructing a wireless communication system using the same.

[0007] The station placement design method relating to this disclosure involves a computer setting conditions for a design area, including the placement of one or more wireless terminals and one or more candidate installation points; generating a population containing multiple individuals such that each of the multiple individuals has genetic information in which a station placement design pattern is encoded by representing the placement or non-placement of a wireless base station at each of the one or more candidate installation points in a binary manner; and performing a generation change to update the multiple individuals in the population using a multi-objective genetic algorithm that finds a Pareto optimal solution as a solution set for the multiple objective functions, each of which has a predetermined priority; and the generation change A base station design method that, after performing the above multiple times, extracts one or more individuals from the multiple individuals included in the population to be included in the solution set, and from the one or more individuals extracted as the solution set, obtains a base station design pattern represented by the genetic information of one individual selected with a higher priority among the multiple objective functions to improve the evaluation value of the objective function with the higher priority, wherein the multiple objective functions include a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that have been selected for installation according to the base station design pattern, with respect to the base station design pattern represented by the genetic information of the target individual.

[0008] The station placement design program relating to this disclosure involves: setting conditions for a design area on a computer, including the placement of one or more wireless terminals and the placement of one or more candidate installation points; generating a population containing multiple individuals such that each of the multiple individuals has genetic information in which a station placement design pattern is encoded by representing the placement or non-placement of a wireless base station at each of the one or more candidate installation points in a binary manner; performing a generation change to update the multiple individuals in the population using a multi-objective genetic algorithm method that finds the Pareto optimal solution as the solution set for the multiple objective functions, each of which has a predetermined priority; and performing the generation change A base station design program that, after performing multiple operations, extracts one or more individuals from the multiple individuals included in the population to be included in the solution set, and from the one or more individuals extracted as the solution set, obtains a base station design pattern represented by the genetic information of one individual selected with a higher priority among the multiple objective functions to prioritize improving the evaluation value of the objective function with the higher priority, wherein the multiple objective functions include a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that have been selected for installation according to the base station design pattern, with respect to the base station design pattern represented by the genetic information of the target individual.

[0009] The station placement design device according to this disclosure includes: a setting unit that sets conditions for the area to be designed, including the placement of one or more wireless terminals and the placement of one or more candidate installation points; a generation unit that generates a population including multiple individuals such that each of the multiple individuals has genetic information in which a station placement design pattern is encoded by representing the placement or non-placement of a wireless base station at each of the one or more candidate installation points in a binary manner; and an update unit that performs generational change to update the multiple individuals included in the population using a multi-objective genetic algorithm method that finds a Pareto optimal solution as a solution set for the multiple objective functions, based on evaluations of each of the multiple individuals included in the population using a multiple objective function, each with a predetermined priority; and The system includes a selection unit that, after performing multiple generational changes, extracts one or more individuals from the multiple individuals included in the population to be included in the solution set, and from the one or more individuals extracted as the solution set, selects one individual that is selected with a higher priority among the multiple objective functions to obtain a site placement design pattern represented by the genetic information of that individual, wherein the multiple objective functions include a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that have been selected for installation according to the site placement design pattern, with respect to the site placement design pattern represented by the genetic information of the target individual.

[0010] The method for constructing a wireless communication system relating to this disclosure includes obtaining a site design pattern by having a computer execute the above-described site design method, and installing a wireless base station in the area to be designed at a location corresponding to one or more candidate installation points that have been designated as installation points according to the obtained site design pattern.

[0011] According to the station design method, station design program, or station design device relating to this disclosure, or the method for constructing a wireless communication system using the same, it becomes possible to satisfy multiple different communication needs required for a target area in a wireless communication system.

[0012] It is a configuration diagram of the wireless communication system according to Embodiment 1. It is a configuration diagram of the base station design device according to Embodiment 1. It is a diagram for explaining an example of base station design for the wireless communication system according to Embodiment 1. It is a block diagram showing an example of the functions of the base station design device according to Embodiment 1. It is a diagram for explaining an example of ranking performed by the update unit according to Embodiment 1. It is a diagram showing an example of communication demand required in the area to be designed by the base station design device according to Embodiment 1. It is a diagram showing an example of communication demand required in the area to be designed by the base station design device according to Embodiment 1. It is a diagram for explaining an example of selection of an individual performed by the selection unit according to Embodiment 1. It is a flowchart showing an example of the operation of the base station design device according to Embodiment 1. It is a flowchart showing an example of the operation of the base station design device according to Embodiment 1.

[0013] Embodiments of the present disclosure will be described with reference to the accompanying drawings. In each figure, the same or corresponding parts are denoted by the same reference numerals, and overlapping descriptions are appropriately simplified or omitted. Note that the present disclosure is not limited to the following embodiments, and within the scope not departing from the gist of the present disclosure, any combination of the embodiments, modification of any component of each embodiment, or omission of any component of each embodiment is possible.

[0014] Embodiment 1. FIG. 1 is a configuration diagram of a wireless communication system 1 according to Embodiment 1.

[0015] The wireless communication system 1 is a system that provides a wireless communication function to each of one or more, that is, one or more wireless terminals 2 in the area to which it is applied. Each wireless terminal 2 is, for example, an information processing terminal device capable of wireless communication. Each wireless terminal 2 may be a general-purpose information processing device such as a tablet computer, a smartphone, or a smartwatch, or may be a vehicle, a robot, a mobility, or other device equipped with a wireless communication module. The wireless terminal 2 may be a device that moves in the target area or a device that is fixedly installed in the target area.

[0016] In the area where the wireless communication system 1 is applied, there is one or more shielding objects 3. In this example, there are multiple shielding objects 3 in the area. The shielding objects 3 are objects that block the wireless signals from the wireless communication system 1. The shielding objects 3 may be fixed objects such as buildings, or moving objects such as vehicles.

[0017] The wireless communication system 1 comprises one or more wireless base stations 4. In this example, the wireless communication system 1 comprises a plurality of wireless base stations 4. Each wireless base station 4 is located in an area to which the wireless communication system 1 is applied. Each wireless base station 4 is equipped with the function of transmitting and receiving wireless signals of the wireless communication system 1. The wireless communication system 1 may comprise a plurality of wireless base stations 4 that correspond to different wireless communication methods. Each wireless base station 4 is configured to communicate wirelessly or wired with equipment inside or outside the wireless communication system 1. Each wireless base station 4 provides wireless communication functionality to a wireless terminal 2 by transmitting and receiving wireless signals to and from the wireless terminal 2.

[0018] The wireless communication system 1 is constructed by actually installing one or more wireless base stations 4 in locations determined by a pre-determined site placement design within the target area. Here, the process of constructing the wireless communication system 1 so that it can operate is sometimes referred to as manufacturing the wireless communication system 1. The site placement design for the wireless communication system 1 is performed, for example, using a site placement design device 5 not shown in Figure 1.

[0019] Figure 2 is a diagram showing the configuration of the site location design device 5 according to Embodiment 1.

[0020] The site design device 5 is, for example, a computer system consisting of one or more server devices, or a device including such a system. Here, a computer system consisting of one or more devices may be simply referred to as a computer. When the site design device 5 is composed of multiple server devices, these multiple server devices may be located in different locations. Some or all of the functions of the site design device 5 may be implemented, for example, by a virtual machine on a cloud service, or by processing or storage resources on a cloud service. The site design device 5 includes a communication unit 6, an external input / output unit 7, a processing unit 8, and a database 9.

[0021] The communication unit 6 communicates with the input / output device 10 located outside the site design device 5 via wired or wireless connection. The input / output device 10 accepts information input from the operator and displays the input information and the information output from the site design device 5 on a display.

[0022] The external input / output unit 7 is an interface between the communication unit 6 and the processing unit 8, and the communication unit 6 inputs information received from the input / output device 10 to the processing unit 8. The external input / output unit 7 also outputs information obtained from calculation processing by the processing unit 8 to the communication unit 6 and transmits it to the input / output device 10 via the communication unit 6.

[0023] The processing unit 8 comprises a processor 11 and a memory 12. The processor 11 is typically a CPU. The memory 12 stores programs that can be executed by the processor 11. The processor 11 is, for example, a CPU, arithmetic unit, microprocessor, or microcomputer. The memory 12 is, for example, a non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, and EEPROM, or a magnetic disk, flexible disk, optical disk, compact disk, minidisc, or DVD. The memory 12 stores programs, for example, as software or firmware. The site design device 5 then performs pre-set processing by having the processor 11 execute the programs stored in the memory 12, and realizes each function as a result of the cooperation between hardware and software. Programs can be stored on computer-readable storage media. Programs can also be provided via a communication network. A program may be a program package that includes multiple subprograms, modules, or libraries. A program is sometimes called a program product. Each function of the site design device 5 may be implemented by a processing circuit. Alternatively, some or all of the functions of the site design device 5 may be implemented together by a processing circuit. Furthermore, the processing circuit may be implemented as, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, or an FPGA, or a combination thereof.

[0024] Database 9 stores information such as calculation conditions or calculation results in site design. Information such as calculation conditions stored in database 9 can be read from database 9 by the processing unit 8 during site design. Various types of information, such as calculation results in site design, can be written to database 9 by the processing unit 8. Various types of information in site design can be written to database 9 by the operator operating the input / output device 10. Information stored in database 9 can be read from database 9 by the operator operating the input / output device 10. Information read from database 9 can be displayed on the input / output device 10 or transmitted externally via a communication network.

[0025] Figure 3 illustrates an example of a site placement design for a wireless communication system 1 according to Embodiment 1.

[0026] In site placement design, for example, the area to which the wireless communication system 1 is applied is designated as the design area, and the number and placement of wireless base stations 4 within the area are determined. The wireless base stations 4 are installed in part or all of one or more candidate installation points 13 located within the area. The candidate installation points 13 are set to locations where wireless base stations 4 can be installed, depending on the area. The candidate installation points 13 are identified, for example, by their location within the area. In site placement design, for example, a site placement design pattern is determined that indicates, among the candidate installation points 13 within the area, at which wireless base stations 4 are installed and at which candidate installation points 13 are not.

[0027] In site placement design, for example, the communication quality when wireless base stations 4 are installed at some or all of the candidate installation points 13 is evaluated. The evaluation of communication quality is performed by simulation of a wireless communication system 1 virtually constructed on the site placement design device 5 for the area under design. At this time, one or more wireless terminals 2 are placed in the virtual area. One or more wireless terminals 2 may be placed, for example, on a grid within the area, randomly, based on previously observed placements, or by other methods.

[0028] Figure 4 is a block diagram showing an example of the functions of the site location design device 5 according to Embodiment 1.

[0029] In this example, the site placement design device 5 performs site placement design for the wireless communication system 1 by solving a combinatorial optimization problem using a multi-objective genetic algorithm. The site placement design device 5 uses, for example, NSGA-II (Non-Dominated Sorting Genetic Algorithm-II), SPEA2 (Strength Parato Evolutionary Approach 2), MSLC (Master-Slave model with Local Cultivation model), or other algorithms as the multi-objective genetic algorithm. The multi-objective genetic algorithm is an algorithm that optimizes to improve the evaluation of site placement design patterns using multiple objective functions. For each objective function, a larger evaluation value is better in the case of an optimization problem that maximizes the objective function, and a smaller evaluation value is better in the case of an optimization problem that minimizes the objective function.

[0030] Here, a first location design pattern is said to be superior to a second location design pattern if, for all objective functions, its evaluation value is better than that of the second location design pattern. On the other hand, a first location design pattern is said not to be superior to a second location design pattern if, for at least one objective function, its evaluation value is not better than that of the second location design pattern. A Pareto optimal solution is a set of location design patterns whose elements are none other than those which are superior to themselves. In this example, the location design device 5 obtains the Pareto optimal solution as a solution set using a multi-objective genetic algorithm.

[0031] Each objective function corresponds to one of several different communication demands required for the area under design. These multiple communication demands include, for example, communication demands for different locations, communication demands with different evaluation metrics, and communication demands with different quality target values. For each objective function, a priority is predetermined according to the strength of the corresponding communication demand requirements.

[0032] The site location design device 5 comprises a setting unit 14, a generation unit 15, an update unit 16, and a selection unit 17 as functional units. The functions of each functional unit of the site location design device 5 are realized, for example, as calculation processing in the processing unit 8.

[0033] The setting unit 14 is a part equipped with a function for setting site design conditions for the area to be designed. Setting site design conditions includes setting the size of a virtual area on the site design device 5, the arrangement of shielding objects 3 within the area, and other calculation conditions. The virtual area on the site design device 5 is set, for example, based on a model of the actual area to be designed. Setting site design conditions includes placing one or more wireless terminals 2 in the virtual area on the site design device 5. Setting site design conditions also includes placing one or more candidate installation points 13 in the virtual area on the site design device 5. The setting unit 14 sets site design conditions, for example, based on information input by the operator to the input / output device 10. The operator inputs, for example, the arrangement of one or more wireless terminals 2 and the arrangement of one or more candidate installation points 13 within the area to the site design device 5 via the input / output device 10.

[0034] The generation unit 15 is a part equipped with the function of generating a population containing multiple individuals as an initial population as a processing of a genetic algorithm. Each individual included in the population has genetic information that represents the corresponding base station design pattern. For this reason, individuals and their genetic information and the corresponding base station design pattern are sometimes represented as identical. The genetic information is information that encodes the base station design pattern. In the genetic information of this example, the base station design pattern is encoded by representing the installation or non-installation of a wireless base station 4 at each candidate installation point 13 with a binary value such as "1" or "0". For example, for numbered candidate installation points 13, the genetic information that encodes a base station design pattern in which a wireless base station 4 is installed at the first candidate installation point 13, not installed at the second candidate installation point 13, and a wireless base station 4 is installed at the third candidate installation point 13 is represented as "101...". The generation unit 15 generates multiple individuals with random genetic information according to the base station design conditions set by the setting unit 14, for example, and uses the population containing the generated multiple individuals as the initial population.

[0035] The update unit 16 is a part equipped with the function of performing generational change processing on a population that includes multiple individuals, such as the initial population generated by the generation unit 15. The generational change processing is a process of updating multiple individuals included in the population using the methods of a multi-objective genetic algorithm. The generational change processing is performed based on evaluation using multiple objective functions for each individual included in the current generation population. The generational change processing includes, for example, processing by ranking, elite preservation, tournament selection, uniform crossover, and mutation.

[0036] Ranking is the process of evaluating the rank of each individual based on factors such as the goodness of fit for multiple objective functions. Ranking is performed, for example, by a non-superiority sort. In this case, individuals with the same rank do not superior to each other. The update unit 16 classifies the individuals included in the current generation population into several groups, for example, according to their proximity to the Pareto optimal solution. The update unit 16 ranks each individual so that individuals in groups closer to the Pareto optimal solution have a higher rank.

[0037] Elite preservation is a process that directly transfers high-ranking individuals from the current generation's population to the next generation's population. In elite preservation, for example, one or more populations are transferred to the next generation's population in order of their rank. This ensures that the individuals included in the population are updated through generational change in a way that approaches a Pareto optimality.

[0038] Tournament selection is the process of selecting one or more individuals from the current generation population. The update unit 16, for example, prioritizes selecting individuals with higher ranks based on ranking in tournament selection. The update unit 16 may also select individuals based on other indicators, such as congestion distance. Uniform crossover is the process of randomly swapping each element of the binary representation of genetic information of two individuals selected from the current generation population by tournament selection or the like, and passing it on to the next generation population. Mutation is the process of randomly transforming the genetic information of individuals selected from the current generation population or individuals that have undergone processes such as uniform crossover, for example, by bit inversion or bit sequence swapping, and passing it on to the next generation population. This diversifies the genetic information of the next generation population.

[0039] The update unit 16 performs elite preservation and then repeatedly performs processes such as tournament selection, uniform crossover, and mutation until the required number of individuals are included in the next generation population, thereby forming the next generation population. The update unit 16 may also perform generational changes using other genetic algorithms, such as roulette selection, ranking selection, two-point crossover, or other processes. The update unit 16 then repeatedly performs generational changes on the updated population. For example, the update unit 16 performs generational changes for a predetermined number of generations.

[0040] The selection unit 17 is a part equipped with the function of selecting one of several individuals included in the latest generation of the population after multiple generations have been performed on the population. The selection unit 17 selects one individual from the population based on, for example, a predetermined criterion. The selection unit 17 extracts a set of solutions obtained as Pareto optimal solutions by, for example, a multi-objective genetic algorithm from the latest generation of the population. From the extracted set of solutions, the selection unit 17 selects one individual in a manner that prioritizes improving the evaluation value by a higher-priority objective function. The selection unit 17 obtains a localization design pattern represented by the genetic information of the selected individual as the result of localization design.

[0041] The operator obtains and refers to the site design results obtained by the selection unit 17 from the site design device 5 via the input / output device 10.

[0042] Next, an example of ranking performed by the update unit 16 will be explained using Figure 5. Figure 5 is a diagram illustrating an example of ranking performed by the update unit 16 according to Embodiment 1.

[0043] In Figure 5, the vertical and horizontal axes represent evaluation values ​​for different objective functions. In this example, location design is performed to minimize each objective function. In Figure 5, each individual in the current generation population is represented by a point on the graph.

[0044] The update unit 16 assigns rank 1 to one or more individuals in the current generation population that are not superior to any other individuals. The population consisting of individuals with the highest rank, rank 1, is the population closest to the Pareto optimal solution in the current generation.

[0045] Next, the update unit 16 assigns rank 2 to one or more individuals that are superior to themselves, excluding the rank 1 population. The population consisting of rank 2 individuals, which is the next highest rank after rank 1, is the population that comes closest to the Pareto optimal solution in the current generation, after the rank 1 population.

[0046] Next, the update unit 16 assigns rank 3 to one or more individuals that are not superior to themselves, excluding the populations of rank 1 and 2. The update unit 16 repeats the same process to rank each individual included in the current generation. In this example, the update unit 16 performs ranking down to the lowest rank, rank X.

[0047] Next, Figures 6 and 7 will be used to explain the multiple objective functions used in the site location design device 5. Figures 6 and 7 show examples of communication demand required in the area to be designed by the site location design device 5 according to Embodiment 1.

[0048] Figure 6 shows the wireless terminals 2 that are placed throughout the entire area under design.

[0049] In the area to be designed, for example, as one of the communication demands, it is required that the communication quality of the entire area be above a predetermined standard. The objective function f corresponding to the requirement for the communication quality of the entire area １ is calculated using the wireless communication quality of the wireless terminal 2 when the wireless base station 4 is installed at the installation candidate point 13 based on the station placement design pattern represented by the genetic information of the target individual. The objective function f １ is represented by, for example, the following formula (1).

[0050]

[0051] Here, the subscript j that is an element of the set J corresponds to each wireless terminal 2. The number of elements |J| of the set J is the number of wireless terminals 2 arranged in the area to be designed in the condition setting of the setting unit 14. The subscript n that is an element of the set N corresponds to each station placement design pattern. The number of elements of the set N is the total number of possible station placement design patterns for one or more installation candidate points 13 arranged in the area to be designed in the condition setting of the setting unit 14. The degree of goal achievement z （１） ｊ，ｎ is a value indicating whether the maximum reception power at the j-th wireless terminal 2 achieves the target value in the station placement design pattern n. The degree of goal achievement z （１） ｊ，ｎ is represented by, for example, the following formula (2).

[0052]

[0053] The maximum reception power p ｊ，ｎ represents the maximum reception power at the j-th wireless terminal 2 in the station placement design pattern n. The target reception power p ｔ１ is the target value of the reception intensity of the wireless signal from the wireless base station 4 at each wireless terminal 2. The target reception power p ｔ１ is set, for example, so that wireless communication at the wireless terminal 2 can be achieved with sufficient quality. The target reception power p ｔ１ is set by a numerical value such as -75 dBm in terms of the RSSI value, for example. The maximum reception power p ｊ，ｎ is the maximum value over the wireless base stations 4 of the reception power r ｊ，ｋ Here, the set K ｎThe subscript k, which is an element of the set, corresponds to each wireless base station 4 that is placed in the area under design in the base station design pattern n. Set K ｎ Number of elements | K ｎ | represents the number of wireless base stations 4 to be placed in the area under design in the base station design pattern n. Received power r ｊ，ｋ r is the received signal strength at the j-th wireless terminal 2 of the radio signal transmitted from the k-th wireless base station 4. Received power r ｊ，ｋ This is calculated, for example, based on the signal strength of the radio signal transmitted by the radio base station 4, the distance between the radio base station 4 and the radio terminal 2, and the presence or absence of an obstruction 3 between the radio base station 4 and the radio terminal 2. Received power r ｊ，ｋ This may be calculated by considering, for example, the reflection of radio signals between the radio base station 4 and the radio terminal 2. The degree of target achievement z for the j-th radio terminal 2. （１） ｊ，ｎ The value of is the maximum received power p ｊ，ｎ Target received power p ｔ１ If this is achieved, the result is 1. Therefore, the objective function f １ This value represents the percentage of wireless terminals 2 whose maximum received power does not reach the target value in the station placement design pattern n, among the one or more wireless terminals 2 placed in the area to be designed in the setting unit 14, i.e., the non-achievement rate. At this time, the station placement design is performed using the objective function f １ It is done in a way that minimizes it.

[0054] Figure 7 shows a wireless terminal 2 located in a specific area within the area under design. This specific area is a pre-defined portion of the area under design. For example, this specific area could be a conference room used for web conferencing. Multiple specific areas may be defined within the area under design.

[0055] In the area under design, for example, one of the communication requirements is that the communication quality in a specific area must be above a predetermined standard. In this example, the requirement for communication quality in the specific area is higher than the requirement for communication quality in the entire area. In this example, the priority of the requirement for communication quality in the specific area is lower than the priority of the requirement for communication quality in the entire area. Objective function f corresponding to the requirement for communication quality in the specific area. ２ This is calculated using the wireless communication quality of the wireless terminal 2 within a specific area when the wireless base station 4 is installed at candidate installation points 13 based on the site placement design pattern represented by the genetic information of the target individual. Objective function f ２ This can be expressed, for example, by the following equation (3).

[0056]

[0057] Here, set J ２ This is a subset of the set J of wireless terminal 2. ２ Each element corresponds to a wireless terminal 2 located within a specific area. Set J ２ Number of elements | J ２ | represents the number of wireless terminals 2 located in a specific area in the setting conditions of the setting unit 14. Target achievement level z （２） ｊ，ｎ This value represents whether the maximum received power of the j-th wireless terminal 2 in the station design pattern n has achieved the target value set for a specific region. Target achievement degree z （２） ｊ，ｎ This can be expressed, for example, by the following equation (4).

[0058]

[0059] Target received power p ｔ２ This is the target value of the received signal strength from the radio base station 4 at each radio terminal 2 located in a specific area. Target received power p ｔ２ For example, this is set so that wireless communication at a wireless terminal 2 in a specific area is possible with sufficient quality. Target received power p ｔ２ This is set by a numerical value such as -60 dBm in RSSI value. Target achievement z for the j-th wireless terminal 2. （２） ｊ，ｎThe value of is the maximum received power p ｊ，ｎ Target received power p ｔ２ If this is achieved, the result is 1. Therefore, the objective function f ２ This value represents the percentage of wireless terminals 2 whose maximum received power does not reach the target value in the station placement design pattern n, among the one or more wireless terminals 2 placed in a specific area in the setting unit 14, i.e., the non-achievement rate. At this time, the station placement design is performed using the objective function f ２ It is done in a way that minimizes it.

[0060] Within the area under design, it may be further required that the communication quality in other specific areas be above a predetermined standard. Within the area under design, multiple objective functions for multiple specific areas may be set independently.

[0061] Other communication needs may be set in the area under design. For example, in the area under design, there may be an additional requirement for a low number of wireless base stations 4 to be installed, regarding the installation efficiency of wireless base stations 4. In this example, the priority of the requirement for the installation efficiency of wireless base stations 4 is lower than the priority of the other communication quality requirements. Objective function f corresponding to the requirement for the installation efficiency of wireless base stations 4 ３ For example, the number of wireless base stations 4 installed when following the site placement design pattern n is |K. ｎ It can be expressed by the following equation (5) using |.

[0062]

[0063] At this time, the site design is based on the objective function f ３ The process is carried out to minimize the following. The objective function f for the installation efficiency of the wireless base station 4 is also mentioned. ３ The objective function f does not necessarily have to be the simple number of wireless base stations 4 installed. For example, depending on the type of wireless base station 4 to be installed at candidate installation points 13, the equipment cost of the wireless base station 4 itself may be considered. ３ This could be the number of wireless base stations 4 weighted according to equipment cost, or the total equipment cost.

[0064] The base station design device 5 stores objective function information in the database 9, such as parameters for each objective function, including target received power and equipment cost for each radio base station 4, as well as the priority of each objective function. The objective function information is set by the setting unit 14 based on information input by the operator to the input / output device 10 when setting conditions for base station design, for example. The setting of objective function information is an example of setting objective functions.

[0065] Next, an example of individual selection by the selection unit 17 will be explained using Figure 8. Figure 8 is a diagram illustrating an example of individual selection performed by the selection unit 17 according to Embodiment 1.

[0066] In Figure 8, each of the three axes has a different objective function f. １ , f ２ , and f ３ These represent the evaluation values ​​for each objective function. In this example, the site design is performed to minimize each objective function. Objective function f １ The priority of the objective function f ２ It has a higher priority than the objective function f. ２ The priority of the objective function f ３ It has a higher priority. In Figure 8, each individual included in the solution set representing the Pareto optimal solution extracted from the latest generation of the multi-objective genetic algorithm is represented by a point on the graph.

[0067] The selection unit 17, after extracting the solution set, selects the objective function f with the highest priority. １ The extracted population is narrowed down to increase the proportion of individuals with better evaluation scores. The selection unit 17, for example, uses the objective function f １ The selection unit 17 narrows down the population to those whose evaluation value is smaller than a preset threshold. Alternatively, the selection unit 17 uses, for example, the objective function f １ The population can be narrowed down to a predetermined number of individuals, ordered by the smallest evaluation value.

[0068] The selection unit 17 is the objective function f １ After narrowing down the options, the next highest priority objective function f ２ The population is further narrowed down so that the proportion of individuals with better evaluation values ​​is increased. The selection unit 17, for example, uses the objective function f２ The selection unit 17 narrows down the population to those whose evaluation value is smaller than a preset threshold. Alternatively, the selection unit 17 uses, for example, the objective function f ２ The population can be narrowed down to a predetermined number of individuals, ordered by the smallest evaluation value.

[0069] The selection unit 17 is the objective function f ２ From the population after narrowing down by the method, the objective function f ２ A lower priority objective function f ３ The selection unit 17 selects the individual with the lowest evaluation value. In this way, the selection unit 17 sequentially narrows down the candidates according to the priority of the objective function, selecting one individual in a way that prioritizes improving the evaluation value of the objective function with a higher priority.

[0070] The selection unit 17 may similarly select individuals even when there are two objective functions to set. For example, the selection unit 17 narrows down the solution set extracted from the latest generation population using the objective function with the higher priority, and then selects one individual from the narrowed population that has the best evaluation value for the other objective function. The selection unit 17 may also similarly select individuals even when there are four or more objective functions to set. For example, the selection unit 17 narrows down the solution set extracted from the latest generation population using the objective function with the highest priority. The selection unit 17 then sequentially narrows down the population using the objective functions in order of priority. After that, the selection unit 17 selects one individual from the narrowed population that has the best evaluation value for the objective function with the lowest priority. The selection unit 17 may also select one individual using only some of the high-priority objective functions among the multiple objective functions used in the search for the Pareto optimal solution.

[0071] Next, we will explain an example of the operation of the site location design device 5 using Figures 9 and 10. Figures 9 and 10 are flowcharts illustrating an example of the operation of the site location design device 5 according to Embodiment 1.

[0072] Figure 9 shows an example of the overall processing in the site design device 5 during site design.

[0073] In step S1, the setting unit 14 places the wireless terminal 2 and candidate installation points 13 in the area to be designed, based on information input by the operator to the input / output device 10, as a condition setting for site placement design. After that, the site placement design device 5 proceeds to step S2.

[0074] In step S2, the setting unit 14 sets objective function information and other settings for multiple objective functions, based on information input by the operator to the input / output device 10, as conditions for setting the site location design. The setting unit 14 may perform the processes of step S1 and step S2 in parallel. After that, the processing of the site location design device 5 proceeds to step S3.

[0075] In step S3, the setting unit 14 calculates the received power at each wireless terminal 2 for the wireless signals transmitted by each wireless base station 4, based on the arrangement of wireless terminals 2 and candidate installation points 13 in the area to be designed, as well as the arrangement of shielding objects 3. After that, the site design device 5 proceeds to step S4.

[0076] In step S4, the site design device 5 searches for Pareto optimal solutions for multiple objective functions using a multi-objective genetic algorithm. In this example, the site design device 5 searches for Pareto optimal solutions that minimize each objective function. After that, the processing of the site design device 5 proceeds to step S5.

[0077] In step S5, the selection unit 17 selects one individual from the Pareto optimal solution obtained by the multi-objective genetic algorithm, prioritizing the improvement of the evaluation value by the higher-priority objective function. The selection unit 17 obtains a location design pattern represented by the genetic information of the selected individual as the result of location design. After storing the result of location design in a database 9 or the like, the processing of the location design device 5 is completed.

[0078] Figure 10 shows an example of the processing performed by the location design device 5 when searching for a Pareto optimal solution using a multi-objective genetic algorithm.

[0079] In step S41, the generation unit 15 generates, for example, multiple individuals with random genetic information. The generation unit 15 generates a population including these multiple individuals as the initial population. After that, the processing of the location design device 5 proceeds to step S42.

[0080] In step S42, the update unit 16 calculates an evaluation value for each individual included in the group based on its respective objective function. Based on the evaluation value for each objective function, the update unit 16 performs a ranking process for each individual. After that, the site design device 5 proceeds to step S43.

[0081] In step S43, the update unit 16 performs generational change processing using the calculated evaluation values ​​of each objective function and the rank of each individual. In this example, as part of the generational change processing, the update unit 16 performs elite preservation processing, passing on individuals with high ranks to the next generation. The update unit 16 also performs processing to pass on individuals that have undergone tournament selection, uniform crossover, and mutation in sequence to the next generation. After the generational change processing has resulted in the required number of individuals being included in the next generation's population, the site design device 5 proceeds to step S44.

[0082] In step S44, the update unit 16 determines whether the current group's generation count has reached a predetermined end generation count. If the end generation count has not been reached, the site design device 5 proceeds to step S42. On the other hand, if the end generation count has been reached, the site design device 5 proceeds to step S45.

[0083] In step S45, the selection unit 17 calculates evaluation values ​​for each objective function for each individual included in the latest generation of the population. The selection unit 17 extracts a set of solutions obtained as Pareto optimal solutions from multiple individuals included in the population, based on the calculated evaluation values ​​for each objective function. After that, the processing of the location design device 5 for the search of location design patterns is completed.

[0084] As described above, the base station design method according to Embodiment 1 is performed by a base station design device 5, which is a computer. The base station design method includes setting conditions for the area to be designed. The condition setting includes the placement of one or more wireless terminals 2 and the placement of one or more candidate installation points 13. The base station design method includes generating a population containing multiple individuals as an initial population. Each individual in the population has genetic information in which a base station design pattern is encoded by representing the installation or non-installation of a wireless base station 4 at each candidate installation point 13 in binary. The base station design method includes performing generational changes to update multiple individuals in the population using a multi-objective genetic algorithm that finds a Pareto optimal solution as a solution set for multiple objective functions based on evaluations using multiple objective functions for each individual in the population. A priority is set in advance for each objective function. After performing generational changes multiple times, the base station design method includes extracting one or more individuals from the multiple individuals in the population that are included in the solution set. The site placement design method includes selecting one individual from among the individuals extracted as a solution set, prioritizing the ability to improve the evaluation value using a higher-priority objective function, thereby obtaining a site placement design pattern represented by the genetic information of that individual. Multiple objective functions include a first objective function. The first objective function is calculated based on the overall wireless communication quality of each wireless terminal 2 when a wireless base station 4 is installed at each candidate installation point 13 designated for installation according to the site placement design pattern represented by the genetic information of the target individual.

[0085] This configuration allows for site placement design as a multi-objective combinatorial optimization problem, enabling site placement designs that satisfy multiple different communication needs. In the site placement design method according to Embodiment 1, the diversity of genetic information in the multi-objective genetic algorithm makes it easier to obtain a global optimal solution than in the case of local search. Furthermore, even when there are many candidate installation points 13, the increase in computational complexity is suppressed compared to the case of exhaustive search. The wireless communication system 1 is constructed by actually installing wireless base stations 4 in the target area at locations determined by the site placement design pattern obtained by the site placement design device 5, etc. This makes it possible to obtain a wireless communication system 1 designed to satisfy multiple communication needs more efficiently.

[0086] Furthermore, the station placement design method includes, when selecting a single individual, narrowing down the one or more individuals extracted as a solution set to one or more individuals so that the proportion of individuals with better evaluation values ​​according to the highest-priority objective function is increased. The station placement design method then includes selecting the single individual with the best evaluation value according to another objective function with a lower priority than the said objective function from the one or more individuals that have been narrowed down so that the proportion of individuals with better evaluation values ​​according to any of the objective functions is increased. In this way, the selection unit 17 sequentially narrows down according to the priority of the objective functions so that it can select a single individual that prioritizes improving the evaluation value according to the higher-priority objective function. Therefore, even if there is a trade-off between objective functions, a single design result corresponding to the priority of multiple communication demands in the area to be designed can be obtained.

[0087] Furthermore, the multiple objective functions include a second objective function in addition to the first objective function. The second objective function is calculated based on the wireless communication quality of wireless terminals 2 in a specific area within the design target area, when wireless base stations 4 are installed at each candidate installation point 13 designated by the site placement design pattern represented by the genetic information of the target individual. By setting the objective functions in this way, site placement design can be efficiently carried out in a way that satisfies both the overall communication quality of the area and the communication quality of the specific area, even when there are areas within the area that require particularly high communication quality. In addition, by setting the priority of the first objective function higher than the priority of the second objective function, site placement design can be efficiently carried out in a way that more reliably satisfies the basic communication quality of the entire area while also satisfying the communication quality of the specific area.

[0088] Furthermore, the multiple objective functions include a third objective function in addition to the first objective function. The third objective function is calculated based on the installation efficiency of the wireless base stations 4 in the site placement design pattern represented by the genetic information of the target individual. The installation efficiency of the wireless base stations 4 is represented, for example, by the number of wireless base stations 4 installed or the total equipment cost. By setting the objective functions in this way, site placement design can be efficiently carried out that satisfies both the requirements for overall communication quality and the installation efficiency of the wireless base stations 4. In addition, by setting the priority of the first objective function higher than the priority of the third objective function, site placement design can be efficiently carried out that pursues the installation efficiency of the wireless base stations 4 while more reliably meeting the basic communication quality requirements for the entire area.

[0089] Furthermore, the candidate installation points 13 may be identified not only by their location within the area, but also by the wireless communication method of the wireless base station 4 to be installed. For the same location within the area, for example, candidate installation points 13 for a wireless base station 4 using a first wireless communication method and candidate installation points 13 for a wireless base station 4 using a second wireless communication method may be set separately. The first wireless communication method is, for example, a wireless communication method using radio signals in the 5.2 GHz band. The second wireless communication method is, for example, a wireless communication method using radio signals in the 28 GHz band. In addition, the candidate installation points 13 may be identified not only by their location within the area, but also by the type of antenna or the direction of the antenna of the wireless base station 4. For example, for the same location within the area, candidate installation points 13 for a wireless base station 4 with its antenna pointed in the first direction and candidate installation points 13 for a wireless base station 4 with its antenna pointed in the second direction may be set separately.

[0090] Furthermore, while the site placement design method was explained using an example of an objective function that evaluates wireless communication quality based on the received power at the wireless terminal 2, other objective functions that evaluate wireless communication quality may also be used in the site placement design method. The objective function may evaluate wireless communication quality based on indicators such as the signal-to-interference and noise ratio or throughput. Estimates of these indicators are calculated, for example, based on the received power at the wireless terminal 2.

[0091] The site design method, site design program, site design device, and construction method relating to this disclosure are applicable to wireless communication systems.

[0092] 1. Wireless communication system, 2. Wireless terminal, 3. Shielding device, 4. Wireless base station, 5. Site design device, 6. Communication unit, 7. External input / output unit, 8. Processing unit, 9. Database, 10. Input / output device, 11. Processor, 12. Memory, 13. Installation candidate points, 14. Setting unit, 15. Generation unit, 16. Update unit, 17. Selection unit

Claims

1. A base station design method comprising: a computer setting conditions for a design area including the placement of one or more wireless terminals and the placement of one or more candidate installation points; generating a population of multiple individuals such that each of the multiple individuals has genetic information that encodes a base station design pattern by representing the placement or non-placement of a wireless base station at each of the one or more candidate installation points in a binary manner; performing generational changes to update the multiple individuals in the population using a multi-objective genetic algorithm that finds a Pareto optimal solution as a solution set for the multiple objective functions, based on evaluations of each of the multiple individuals in the population using a plurality of objective functions, each with a predetermined priority; extracting one or more individuals from the multiple individuals in the population that are included in the solution set after performing the generational changes multiple times; and obtaining a base station design pattern represented by the genetic information of one individual selected from the one or more individuals extracted as the solution set, prioritizing the improvement of the evaluation value by the objective function with the higher priority among the plurality of objective functions, wherein the plurality of objective functions are A site placement design method that includes a site placement design pattern represented by the genetic information of a target individual, and a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that are designated as installation points according to the site placement design pattern.

2. The location design method according to claim 1, wherein when selecting one individual from one or more individuals extracted as the solution set to obtain a location design pattern, the computer performs the following: narrowing down the one or more individuals extracted as the solution set to one or more individuals such that the proportion of individuals whose evaluation value is better with the objective function with the highest priority among the multiple objective functions is greater; and from the one or more individuals narrowed down such that the proportion of individuals whose evaluation value is better with any of the multiple objective functions is greater, the computer selects one individual whose evaluation value is best with another objective function with a lower priority than that objective function.

3. The location design method according to claim 1, wherein the plurality of objective functions include a second objective function calculated based on the wireless communication quality of some of the wireless terminals in a predetermined area among the one or more wireless terminals when a wireless base station is installed at each of the installation candidate points that have been designated as installation locations among the one or more installation candidate points, with respect to the location design pattern represented by the genetic information of the target individual.

4. The priority of the first objective function is higher than the priority of the second objective function, the location design method according to claim 3.

5. The base station design method according to claim 1, wherein the plurality of objective functions include a third objective function calculated based on the installation efficiency of radio base stations in a base station design pattern represented by the genetic information of the target individual, and the priority of the first objective function is higher than the priority of the third objective function.

6. A station design program that causes a computer to perform the following: setting conditions for an area to be designed, including the placement of one or more wireless terminals and the placement of one or more candidate installation points; generating a population containing multiple individuals such that each of the multiple individuals has genetic information that encodes a station design pattern by representing the placement or non-placement of a wireless base station at each of the one or more candidate installation points in a binary manner; performing generational changes to update the multiple individuals in the population using a multi-objective genetic algorithm that finds a Pareto optimal solution as a solution set for the multiple objective functions, based on evaluations of each of the multiple individuals in the population using a multiple objective function, each with a predetermined priority; extracting one or more individuals from the multiple individuals in the population that are included in the solution set after performing the generational changes multiple times; and obtaining a station design pattern represented by the genetic information of one individual selected from the one or more individuals extracted as the solution set, prioritizing the improvement of the evaluation value by the objective function with the higher priority among the multiple objective functions, wherein the multiple objective functions are A site placement design program that includes a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that have been designated as installation points according to the site placement design pattern, with respect to a site placement design pattern represented by the genetic information of the target individual.

7. A setting unit that sets conditions for the area to be designed, including the placement of one or more wireless terminals and the placement of one or more candidate installation points; a generation unit that generates a population including multiple individuals such that each of the multiple individuals has genetic information that encodes a base station design pattern by representing the installation or non-installation of a wireless base station at each of the one or more candidate installation points in a binary manner; an update unit that performs generational changes to update the multiple individuals included in the population using a multi-objective genetic algorithm method that finds a Pareto optimal solution as a solution set for the multiple objective functions, based on evaluations of each of the multiple individuals included in the population using a plurality of objective functions, each with a predetermined priority; a selection unit that, after performing the generational changes multiple times, extracts one or more individuals included in the solution set from the multiple individuals included in the population, and obtains a base station design pattern represented by the genetic information of one individual selected from the one or more individuals extracted as the solution set, prioritizing the improvement of the evaluation value by the objective function with the higher priority among the multiple objective functions; wherein the plurality of objective functions are A site placement design device that includes a site placement design pattern represented by the genetic information of a target individual, and a first objective function calculated based on the overall wireless communication quality of one or more wireless terminals when a wireless base station is installed at each of the one or more candidate installation points that have been designated as installation points according to the site placement design pattern.

8. A method for constructing a wireless communication system, comprising: having a computer execute the site location design method described in claim 1 to obtain a site location design pattern; and installing a wireless base station in the area to be designed at a location corresponding to one or more candidate installation points that have been designated as installation points in the obtained site location design pattern.

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