Base station layout method, device and system, equipment and storage medium

Abstract

The invention relates to a base station layout method, device and system, equipment and a storage medium. The method comprises the following steps: generating an initial random layout for each base station in a set area; performing adaptability evaluation on each initial random layout by adopting a preset evaluation index to obtain an initial population; performing iterative optimization on the initial population by adopting a genetic algorithm model, gradually optimizing the base station layout until a preset condition is met, and taking the genetic algorithm model at the moment as a trained layout model; and performing layout for each base station in the target area by adopting the trained layout model. The layout scheme is automatically generated for the base station based on various factors by simulating natural selection and genetic mechanisms, so that the problem of unreasonable layout of the base station in the prior art is solved, and the overall performance of a communication network is improved.

Description

Technical Field

[0001] The present application relates to the field of mobile communication technology, and in particular to a method, device, system, equipment and storage medium for deploying a base station. Background Art

[0002] By setting up multiple base stations at appropriate locations, the mobile communication network provides users with higher transmission rates, wider coverage, and greater capacity.

[0003] In the field of wireless communications, the reasonable layout of base stations is crucial to improving signal coverage quality, reducing interference and operating costs. Traditional technologies mainly use manual experience or simple optimization algorithms to layout base stations, which has problems such as uneven coverage, severe interference and high costs. For example, in densely populated areas, the layout of base stations needs to take into account the obstruction of buildings and the diversity of user needs, which poses a challenge to existing technologies. Summary of the invention

[0004] Based on this, it is necessary to provide a base station layout method, device, system, equipment and storage medium to address the above technical issues.

[0005] In a first aspect, the present application provides a method for deploying base stations. The method comprises:

[0006] For each base station in a set area, an initial random layout is generated for it;

[0007] Using a preset evaluation index, the adaptability of each of the initial random layouts is evaluated to obtain an initial population;

[0008] Iteratively optimizing the initial population using a genetic algorithm model, gradually optimizing the base station layout until a preset condition is met, and using the genetic algorithm model at this time as a trained layout model;

[0009] Use the trained layout model to layout base stations in the target area.

[0010] In one embodiment, generating an initial random layout for each base station in a set area includes:

[0011] Randomly assign a coordinate position within a set area to each base station;

[0012] Randomly setting a network parameter for each base station, wherein the network parameter includes transmission power and frequency;

[0013] Based on the coordinate positions and network parameters, an initial random layout is generated.

[0014] In one of the embodiments, the preset evaluation indicators include signal coverage, interference level and cost;

[0015] The method of using a preset evaluation index to perform adaptability evaluation on each of the initial random layouts to obtain an initial population includes:

[0016] Assigning a weight to each of the preset evaluation indicators;

[0017] According to the assigned weights, a weighted sum of the preset evaluation indicators in each of the initial random layouts is calculated to obtain a fitness value of each of the initial random layouts;

[0018] Among them, each of the initial random layouts carrying a fitness value constitutes the initial population.

[0019] In one embodiment, the iterative optimization of the initial population using a genetic algorithm model includes:

[0020] For each iteration, the genetic algorithm model performs selection, crossover and mutation operations on the initial population to obtain a new candidate population, and generates a new layout solution based on the new candidate population;

[0021] Use the preset evaluation index to evaluate the adaptability of the new layout scheme and obtain a new fitness value;

[0022] The best layout scheme among the new fitness values ​​is selected to form a new population, which is used as the population for the next iteration.

[0023] In one embodiment, a genetic algorithm model is used to perform selection, crossover, and mutation operations on the initial population to obtain a new candidate population, including:

[0024] Based on the fitness value corresponding to each initial random layout, the initial random layout corresponding to the fitness value that meets the preset value is selected as the parent individual;

[0025] Randomly select two parent individuals from all parent individuals, randomly select a crossover point in the gene sequences of the two parent individuals, and exchange the gene fragments of the two parent individuals at the crossover point to form two new offspring individuals;

[0026] According to the preset mutation frequency, multiple individuals are selected from all new offspring individuals, and their genetic parameters are randomly adjusted to obtain a new candidate group.

[0027] In one embodiment, the method of determining whether the preset condition is met includes:

[0028] If the number of iterations meets the preset number or the maximum change of the fitness value of the current population is less than the preset convergence threshold, it is considered that the preset condition is met.

[0029] In a second aspect, the present application also provides a base station layout device. The device includes:

[0030] A first layout module, used to generate an initial random layout for each base station in a set area;

[0031] An evaluation module, used to use a preset evaluation index to perform adaptability evaluation on each of the initial random layouts to obtain an initial population;

[0032] A training module, used to iteratively optimize the initial population using a genetic algorithm model, gradually optimize the base station layout until a preset condition is met, and use the genetic algorithm model at this time as a trained layout model;

[0033] The second layout module is used to use the trained layout model to layout the base stations in the target area.

[0034] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method steps described in any one of the first aspects are implemented.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method steps described in any one of the first aspects are implemented.

[0036] The above-mentioned base station layout method, device, system, equipment and storage medium have at least the following advantages:

[0037] This application generates an initial random layout for each base station in a set area; uses preset evaluation indicators to evaluate the adaptability of each initial random layout to obtain an initial population; uses a genetic algorithm model to iteratively optimize the initial population, gradually optimize the base station layout until the preset conditions are met, and uses the genetic algorithm model at this time as a trained layout model; uses the trained layout model to layout each base station in the target area. This application simulates natural selection and genetic mechanisms to automatically generate a layout plan for the base station based on multiple factors, thereby solving the problem of unreasonable base station layout in the prior art and improving the overall performance of the communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 is a structural schematic diagram of a base station layout system in one embodiment;

[0041] Figure 2 is a schematic flow chart of a method for deploying base stations in one embodiment;

[0042] Figure 3 A schematic diagram of a process for generating an initial random layout step in one embodiment;

[0043] Figure 4 A schematic diagram of a flow chart of a step of performing adaptability evaluation on an initial random layout in one embodiment;

[0044] Figure 5 A schematic diagram of a flow chart of an iterative optimization step for an initial population in one embodiment;

[0045] Figure 6 A schematic diagram of a flow chart of selection, crossover and mutation operation steps in one embodiment;

[0046] Figure 7 is a structural block diagram of a base station layout device in one embodiment;

[0047] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0049] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0050] See also Figure 1In an exemplary embodiment, the present application provides a base station layout system, including multiple base stations 102 and a server 104.

[0051] Specifically, the base station 102 is an important part of the mobile communication network, which is used to communicate wirelessly with mobile terminals and send and receive signals. The server 104 is a computing device or computer program used to store, process or transmit data, which is usually deployed in a data center and connected to the Internet or an enterprise intranet through a wired network (such as optical fiber, Ethernet, etc.). Each base station 102 is connected to the server 104 by wire or wireless means and exchanges data.

[0052] During the data interaction between each base station 102 and the server 104, the base station 102 may transmit network parameters to the server 104, wherein the network parameters include signal strength, coordinate position, network traffic, transmission power, frequency, and user information. Further, after obtaining the above network parameters, the server 104 may generate an initial random layout for each base station in the set area according to the network parameters; use the preset evaluation index to perform adaptability evaluation on each initial random layout to obtain an initial population; use the genetic algorithm model to iteratively optimize the initial population, gradually optimize the base station layout until the preset conditions are met, and use the genetic algorithm model at this time as a trained layout model; use the trained layout model to layout each base station 102 in the target area.

[0053] For example, the mobile terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc.

[0054] Optionally, an encrypted channel is established between each base station 102 and the server 104 to ensure the security of data transmission. Exemplarily, the embodiment of the present application uses TLS (Transport Layer Security Protocol) to build an encrypted channel. Specifically, TLS protects the confidentiality, integrity and source reliability of communication data through encryption and identity authentication.

[0055] The above-mentioned base station layout system generates an initial random layout for each base station in a set area; uses preset evaluation indicators to evaluate the adaptability of each initial random layout to obtain an initial population; uses a genetic algorithm model to iteratively optimize the initial population, gradually optimizes the base station layout, until the preset conditions are met, and uses the genetic algorithm model at this time as a trained layout model; uses the trained layout model to layout each base station in the target area. This application simulates natural selection and genetic mechanisms to automatically generate a layout plan for the base station based on multiple factors, thereby solving the problem of unreasonable base station layout in the prior art and improving the overall performance of the communication network.

[0056] In an exemplary embodiment, the present application provides a method for deploying a base station. Figure 1 The server 104 in FIG. 1 is used as an example for explanation.

[0057] See also Figure 2 , Figure 2 The following is a flow chart of a method for deploying base stations according to the present embodiment, which specifically includes the following steps:

[0058] Step S202: generating an initial random layout for each base station in a set area.

[0059] Specifically, the set area is an area selected according to project needs. Multiple base stations are set up in the area, and each base station is communicated with the server. The server can layout each base station to determine its location and network parameters, and adjust the layout plan in real time according to the feedback from each base station to achieve optimal layout.

[0060] The initial random layout is a layout randomly generated by the server for each base station. This layout is used as the starting point and is gradually adjusted based on the feedback from each base station on the layout.

[0061] Step S204, using a preset evaluation index to perform adaptability evaluation on each initial random layout to obtain an initial population.

[0062] Specifically, each initial random layout is evaluated to confirm the performance of the solution. Generally speaking, a higher evaluation score indicates a better performance of the solution.

[0063] The selection of evaluation indicators depends on the optimization goal. Generally speaking, the evaluation parameters for base stations include signal coverage area, interference minimization, power efficiency, user service quality, etc. By comprehensively evaluating the above indicators, an evaluation value can be obtained.

[0064] The initial population refers to the initial samples used to train the model, which is composed of the above-mentioned multiple initial random layouts and the evaluation values ​​corresponding to each initial random layout.

[0065] Step S206, iteratively optimize the initial population using a genetic algorithm model, gradually optimize the base station layout until the preset conditions are met, and use the genetic algorithm model at this time as a trained layout model.

[0066] Specifically, a genetic algorithm (GA) solves optimization problems by simulating the natural evolution process. In the embodiment of the present application, a genetic algorithm is used to iteratively optimize the layout scheme of the base station. In each iteration, multiple layout schemes with better performance are selected as the population for the next iteration, and eventually the optimal layout scheme is tended to be found.

[0067] It should be understood that in the above iterative optimization process, the optimal layout parameters of the layout scheme, such as position, frequency, power, etc., can be obtained through the genetic algorithm. At the same time, the core parameters of the genetic algorithm model used in training, such as population size, selection method, crossover probability, mutation probability, etc., will also be optimized. At the end of the iteration, the core parameters obtained at this time are used as the hyperparameters of the layout model.

[0068] Optionally, the determination method of satisfying the preset condition includes:

[0069] If the number of iterations meets the preset number or the maximum change of the fitness value of the current population is less than the preset convergence threshold, it is considered that the preset condition is met.

[0070] Step S208: Use the trained layout model to layout the base stations in the target area.

[0071] Specifically, before deploying new base stations in the target area, first obtain the feasible location information of the new base stations in the target area, as well as the environmental information around each location, where the environmental information includes buildings, terrain, population density, etc.; in addition, if there are existing base stations in the target area, it is also necessary to obtain the distribution of each existing base station; further, it is also necessary to obtain the signal demand, traffic demand, interference situation and other network parameters in the target area.

[0072] Based on the information obtained above, a layout model is used to generate a layout plan. It should be understood that after obtaining the layout plan, the layout plan can also be evaluated, and then the layout plan can be fine-tuned according to the evaluation results and user feedback until the project needs are met.

[0073] The above-mentioned base station layout method generates an initial random layout for each base station in a set area; uses preset evaluation indicators to evaluate the adaptability of each initial random layout to obtain an initial population; uses a genetic algorithm model to iteratively optimize the initial population, gradually optimizes the base station layout, until the preset conditions are met, and uses the genetic algorithm model at this time as a trained layout model; uses the trained layout model to layout each base station in the target area. This application simulates natural selection and genetic mechanisms to automatically generate a layout plan for the base station based on multiple factors, thereby solving the problem of unreasonable base station layout in the prior art and improving the overall performance of the communication network.

[0074] See also Figure 3 Optionally, for each base station in the set area, an initial random layout is generated for it, including:

[0075] Step S302: randomly assign a coordinate position within a set area to each base station.

[0076] Step S302: randomly set a network parameter for each base station, where the network parameter includes transmission power and frequency.

[0077] Step S304: Generate an initial random layout based on the coordinate positions and network parameters.

[0078] Exemplarily, first, multiple locations where base stations can be set are selected in a set area, and each coordinate position belongs to the above selected location. Secondly, a range of network parameters in the set area is set, and a parameter value is randomly assigned to each base station within the set range. It should be understood that the selection of the above positions and parameter ranges is carried out in accordance with technical specifications and actual engineering constraints.

[0079] For example, in a 10x10 area, 10 base station layout plans are generated, each plan contains the locations and parameters of 5 base stations. Then layout plan 1: [(1,2), (3,4), (5,5), (7,8), (9,1)]; layout plan 2: [(2,3), (4,5), (6,6), (8,9), (1,2)].

[0080] See also Figure 4 Optionally, the preset evaluation indicators include signal coverage, interference level and cost. The preset evaluation indicators are used to evaluate the adaptability of each initial random layout to obtain an initial population, including:

[0081] Step S402: assign a weight to each preset evaluation indicator.

[0082] Step S404, calculating the weighted sum of each preset evaluation index in each initial random layout according to the assigned weight, and obtaining the fitness value of each initial random layout; wherein each initial random layout carrying the fitness value constitutes an initial population.

[0083] Specifically, the weight coefficient is used to characterize the importance of each indicator in the fitness evaluation. In the embodiment of the present application, signal coverage, interference level and cost are selected for evaluation. In practical applications, other indicators can also be selected for evaluation as needed.

[0084] For example, the signal propagation model is used to calculate the signal coverage of users under each base station layout to obtain the coverage rate. The signal propagation model is a mathematical expression used to describe the changes in parameters such as signal strength, attenuation, and propagation distance during the process of radio waves being transmitted from the base station to the user equipment. This model is crucial for predicting the signal coverage of users under the base station layout, and thus the coverage rate can be calculated.

[0085] The expression of coverage is:

[0086]

[0087] Furthermore, based on the distance between base stations and the frequency reuse situation, the mutual interference degree between base stations is evaluated, where the expression of the interference level is:

[0088]

[0089] Among them, I ij represents the interference effect of base station i on base station j, C ij is the distance between base station i and base station j and the frequency reuse factor, and N is the number of base stations.

[0090] According to the base station construction and operation cost model, the total cost is calculated, where the cost expression is:

[0091]

[0092] Among them, C 建设,i and C 运营,i They represent the construction cost and operation cost of base station i respectively.

[0093] Based on the above indicators, a fitness function is constructed, which represents the weighted sum of the preset evaluation indicators, and its expression is: fitness = w1×coverage-w2×interference level+w3×cost. Among them, w1, w2, and w3 are weight coefficients.

[0094] For example, in a 10x10 area, 10 base station layout plans are generated, each plan contains the locations and parameters of 5 base stations.

[0095] Use the signal propagation model to calculate the coverage of each base station under the current layout and determine the number of users covered.

[0096] Based on the distance and frequency configuration between base stations, the interference degree between base stations is evaluated and a quantitative indicator of the interference level is obtained.

[0097] Based on the base station construction and operation cost model, calculate the total cost of each base station.

[0098] Substitute the above calculation results into the fitness function to obtain the fitness value of each layout scheme.

[0099] The above-mentioned base station layout method generates an initial random layout for each base station in a set area, and uses evaluation indicators such as signal coverage, interference level and cost to evaluate each initial random layout to determine the performance of each initial random layout. The initial population composed of the above-mentioned initial random layout and its corresponding fitness value can be used as the basis for subsequent model training.

[0100] See also Figure 5 Optionally, a genetic algorithm model is used to iteratively optimize the initial population, including:

[0101] Step S502: For each iteration, the genetic algorithm model performs selection, crossover and mutation operations on the initial population to obtain a new candidate population, and generates a new layout solution based on the new candidate population.

[0102] Step S504: Use a preset evaluation index to evaluate the adaptability of the new layout solution to obtain a new fitness value.

[0103] Step S506, screen out the best layout solution among the new fitness values ​​to form a new population, which is used as the population for the next iteration.

[0104] Specifically, in each iteration, the genetic algorithm model will optimize the population obtained in the previous iteration, generate a new layout plan based on the optimized new population, and then evaluate the new layout plan, and select the layout plan with better performance to form a new population. It should be noted that the new population can completely replace the old population with the selected layout plan, or it can partially replace it.

[0105] See also Figure 6 Optionally, a genetic algorithm model is used to perform selection, crossover, and mutation operations on the initial population to obtain a new candidate population, including:

[0106] Step S602: based on the fitness value corresponding to each initial random layout, an initial random layout corresponding to a fitness value that meets a preset value is selected as a parent individual.

[0107] Specifically, the selection operation of the embodiment of the present application adopts a roulette wheel selection method, which allocates reproduction opportunities based on the fitness values ​​of individuals, wherein the individuals represent each layout scheme in the population.

[0108] The specific steps include:

[0109] First, the fitness values ​​of all individuals are normalized to the interval [0,1] to ensure that individuals with higher fitness values ​​have a greater probability of being selected. Then, a roulette wheel is constructed based on the normalized fitness values, and the sector area of ​​each individual is proportional to its fitness value. Finally, the roulette wheel is rotated multiple times, and each rotation randomly selects an individual as the parent for reproduction. It should be noted that in addition to the above-mentioned roulette selection method, common strategies for selection operations also include tournament selection and ranking selection. In practical applications, appropriate strategies can be selected according to needs.

[0110] Step S604, randomly select two parent individuals from all parent individuals, randomly select a crossover point in the gene sequences of the two parent individuals, and exchange the gene fragments of the two parent individuals at the crossover point to form two new offspring individuals.

[0111] Specifically, the crossover operation in the embodiment of the present application is implemented by a single-point crossover method, exchanging some genes of two parent individuals to generate new offspring individuals. The specific steps include:

[0112] First, a crossover point is randomly selected from the gene fragments of the two parent individuals, and the gene of the parent individual is divided into two parts. Secondly, at the crossover point, the gene fragments of the two parent individuals are exchanged to form two new offspring individuals. It should be understood that during the operation, it should be ensured that the newly generated offspring individuals are within the technical specifications and the integrity of the genes is maintained. It should be noted that the crossover operation method includes multi-point crossover and uniform crossover in addition to the above-mentioned single-point crossover. In practical applications, the appropriate method can be selected as needed.

[0113] For example, the above layout scheme 1: [(1,2), (3,4), (5,5), (7,8), (9,1)] and layout scheme 2: [(2,3), (4,5), (6,6), (8,9), (1,2)] have higher fitness values ​​and are selected by the selection operation as parent 1 and parent 2 respectively. Based on single-point crossover, the cut is made at the third position and some genes are exchanged.

[0114] Parent 1: [(1,2),(3,4),(5,5),(7,8),(9,1)]

[0115] Parent 2: [(2,3),(4,5),(6,6),(8,9),(1,2)]

[0116] Generate offspring 1: [(1,2),(3,4),(5,5),(8,9),(1,2)]

[0117] Generate offspring 2: [(2,3),(4,5),(6,6),(7,8),(9,1)]

[0118] It can be seen that the genes after the third position of the above two parent individuals are cut and exchanged with each other to form offspring 1 and offspring 2.

[0119] Step S606, selecting multiple individuals from all new offspring individuals according to a preset mutation frequency, and randomly adjusting their gene parameters to obtain a new candidate group.

[0120] Specifically, the mutation operation in the embodiment of the present application randomly adjusts the newly generated offspring individuals to introduce new genetic diversity. The specific steps include:

[0121] First, individuals are selected from the offspring for mutation according to the preset mutation probability. Second, one or more gene values ​​of the selected individuals are randomly adjusted, such as base station location, power or frequency. It should be understood that during the operation, the adjusted gene values ​​must meet the preset technical specifications and actual engineering limitations.

[0122] Exemplarily, a random mutation operation is performed on the newly generated offspring, for example, a base station position of the offspring 1 is changed from (1, 2) to (2, 2).

[0123] Through the above selection, crossover and mutation operations, a new group of offspring individuals can be obtained, which will be further screened in the next round of fitness evaluation to effectively explore the space of base station layout solutions and gradually approach the optimal solution.

[0124] Optionally, training the genetic algorithm model to obtain a trained layout model also includes: designing an iteration process and designing a termination condition.

[0125] Among them, the steps of designing the iterative process include: loop iteration, fitness update and population update.

[0126] Loop iteration: Set up a loop structure. In each iteration, the population will undergo selection, crossover, and mutation operations to generate a new generation of base station layout solutions.

[0127] Fitness update: After each iteration, the fitness value of the new generation population is recalculated to evaluate its performance.

[0128] Population update: According to the fitness value of the new generation population, update the current optimal solution and replace or merge it into the original population.

[0129] The steps of designing the termination condition include: setting a maximum number of iterations or setting a convergence threshold.

[0130] Maximum number of iterations: Set a maximum number of iterations N. When the number of iterations reaches N, the iterative process terminates.

[0131] Fitness convergence: Set a convergence threshold θ. When the maximum change in the population fitness value is less than θ in M ​​consecutive iterations, the fitness is considered to have converged and the iterative process is terminated.

[0132] It should be understood that the iteration is stopped when any of the above termination conditions is met. For example, the current iteration number is the 20th, and the maximum number of iterations is set to 100. In this iteration, a new generation of base station layout solutions is generated through genetic operations, and a new fitness value is calculated. Assuming that the fitness change for 5 consecutive iterations is less than 0.01 and the set convergence threshold θ is reached, the iteration process is terminated.

[0133] Based on the above-designed solution, the training of the genetic algorithm model can be completed through the following steps to obtain a trained layout model:

[0134] Step 1: Select excellent individuals from the current population based on fitness.

[0135] Step 2: Perform cross operation on the selected individuals to generate a new layout plan.

[0136] Step 3: Perform mutation operations on the newly generated layout scheme to increase the diversity of the population.

[0137] Step 4: Calculate the fitness value of the new generation population and record the current optimal solution.

[0138] Step 5: Check whether the termination condition is met. If not, return to step 1 and continue the next round of iteration.

[0139] The above-mentioned base station layout method generates an initial random layout for each base station in the set area, and uses evaluation indicators such as signal coverage, interference level and cost to evaluate each initial random layout to determine the performance of each initial random layout. The genetic algorithm model is based on the initial population, and performs selection, crossover and mutation operations on it to obtain a group of new offspring individuals. These individuals will be further screened in the next round of fitness evaluation to effectively explore the space of base station layout solutions and gradually approach the optimal solution. This optimal solution will include a set of base station coordinates, power and frequency settings and other parameters, which together achieve the highest signal coverage and the lowest interference level, while taking into account cost factors. Through this iterative optimization method, this solution can provide an efficient and practical optimization path for base station layout.

[0140] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0141] Based on the same inventive concept, the embodiment of the present application also provides a base station layout device for implementing the above-mentioned base station layout method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the layout device for one or more base stations provided below can refer to the limitations of the base station layout method above, and will not be repeated here.

[0142] See also Figure 7 In an exemplary embodiment, the present application provides a base station layout device, including: a first layout module, an evaluation module, a training module, and a second layout module, wherein:

[0143] The first layout module is used to generate an initial random layout for each base station in a set area.

[0144] The evaluation module is used to use preset evaluation indicators to evaluate the adaptability of each initial random layout to obtain an initial population.

[0145] The training module is used to iteratively optimize the initial population using a genetic algorithm model, gradually optimize the base station layout until the preset conditions are met, and use the genetic algorithm model at this time as a trained layout model.

[0146] The second layout module is used to use the trained layout model to layout the base stations in the target area.

[0147] Optionally, the first layout module generates an initial random layout for each base station within the set area, including: randomly assigning a coordinate position within the set area to each base station; randomly setting a network parameter for each base station, the network parameter including transmission power and frequency; and generating the initial random layout based on the coordinate position and the network parameter.

[0148] Optionally, the preset evaluation indicators include signal coverage, interference level and cost. The evaluation module uses the preset evaluation indicators to perform adaptability evaluation on each initial random layout to obtain an initial population, including: assigning a weight to each preset evaluation indicator; according to the assigned weight, calculating the weighted sum of each preset evaluation indicator in each initial random layout to obtain the fitness value of each initial random layout; wherein each initial random layout carrying a fitness value constitutes the initial population.

[0149] Optionally, the training module uses a genetic algorithm model to iteratively optimize the initial population, including: for each iteration, the genetic algorithm model performs selection, crossover and mutation operations on the initial population to obtain a new candidate population, and generates a new layout plan based on the new candidate population; uses preset evaluation indicators to evaluate the adaptability of the new layout plan to obtain a new fitness value; screens out the optimal layout plan among the new fitness values ​​to form a new population, which is used as the population for the next iteration.

[0150] Optionally, the training module uses a genetic algorithm model to perform selection, crossover, and mutation operations on the initial population to obtain a new candidate group, including: based on the fitness value corresponding to each initial random layout, screening out the initial random layout corresponding to the fitness value that meets the preset value as the parent individual; randomly selecting two parent individuals from all parent individuals, randomly selecting an intersection point in the gene sequences of the two parent individuals, and at the intersection point, exchanging the gene fragments of the two parent individuals to form two new offspring individuals; according to the preset mutation frequency, selecting multiple individuals from all new offspring individuals, randomly adjusting their genetic parameters, to obtain a new candidate group.

[0151] The above-mentioned base station layout device generates an initial random layout for each base station in the set area, and uses evaluation indicators such as signal coverage, interference level and cost to evaluate each initial random layout to determine the performance of each initial random layout. The genetic algorithm model is based on the initial population, and performs selection, crossover and mutation operations on it to obtain a group of new offspring individuals. These individuals will be further screened in the next round of fitness evaluation to effectively explore the space of base station layout solutions and gradually approach the optimal solution. This optimal solution will include a set of base station coordinates, power and frequency settings and other parameters, which together achieve the highest signal coverage and the lowest interference level, while taking into account cost factors. Through this iterative optimization method, this solution can provide an efficient and practical optimization path for base station layout.

[0152] Each module in the above-mentioned base station layout device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0153] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for arranging a base station is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0154] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0155] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned base station layout method when executing the computer program.

[0156] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned base station layout method are implemented.

[0157] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned base station layout method are implemented.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0159] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for deploying base stations, characterized in that: The method comprises: For each base station in a set area, an initial random layout is generated for it; Using a preset evaluation index, the adaptability of each of the initial random layouts is evaluated to obtain an initial population; Iteratively optimizing the initial population using a genetic algorithm model, gradually optimizing the base station layout until a preset condition is met, and using the genetic algorithm model at this time as a trained layout model; Use the trained layout model to layout base stations in the target area.

2. The method according to claim 1, characterized in that The step of generating an initial random layout for each base station in the set area includes: Randomly assign a coordinate position within a set area to each base station; Randomly setting a network parameter for each base station, wherein the network parameter includes transmission power and frequency; Based on the coordinate positions and network parameters, an initial random layout is generated.

3. The method according to claim 2, characterized in that The preset evaluation indicators include signal coverage, interference level and cost; The method of using a preset evaluation index to perform adaptability evaluation on each of the initial random layouts to obtain an initial population includes: Assigning a weight to each of the preset evaluation indicators; According to the assigned weights, a weighted sum of the preset evaluation indicators in each of the initial random layouts is calculated to obtain a fitness value of each of the initial random layouts; Among them, each of the initial random layouts carrying a fitness value constitutes the initial population.

4. The method according to claim 1, characterized in that: The iterative optimization of the initial population using a genetic algorithm model includes: For each iteration, the genetic algorithm model performs selection, crossover and mutation operations on the initial population to obtain a new candidate population, and generates a new layout solution based on the new candidate population; Use the preset evaluation index to evaluate the adaptability of the new layout scheme and obtain a new fitness value; The best layout scheme among the new fitness values ​​is selected to form a new population, which is used as the population for the next iteration.

5. The method according to claim 4, characterized in that: The genetic algorithm model is used to perform selection, crossover and mutation operations on the initial population to obtain a new candidate population, including: Based on the fitness value corresponding to each initial random layout, the initial random layout corresponding to the fitness value that meets the preset value is selected as the parent individual; Randomly select two parent individuals from all parent individuals, randomly select a crossover point in the gene sequences of the two parent individuals, and exchange the gene fragments of the two parent individuals at the crossover point to form two new offspring individuals; According to the preset mutation frequency, multiple individuals are selected from all new offspring individuals, and their genetic parameters are randomly adjusted to obtain a new candidate group.

6. The method according to claim 1, characterized in that The determination method of satisfying the preset condition includes: If the number of iterations meets the preset number or the maximum change of the fitness value of the current population is less than the preset convergence threshold, it is considered that the preset condition is met.

7. A base station layout device, characterized in that: The device comprises: A first layout module, used to generate an initial random layout for each base station in a set area; An evaluation module, used to use a preset evaluation index to perform adaptability evaluation on each of the initial random layouts to obtain an initial population; A training module, used to iteratively optimize the initial population using a genetic algorithm model, gradually optimize the base station layout until a preset condition is met, and use the genetic algorithm model at this time as a trained layout model; The second layout module is used to use the trained layout model to layout the base stations in the target area.

8. A base station layout system, characterized in that: The system comprises: a service end and a plurality of base stations; The server uses the base station layout method described in any one of claims 1 to 6 to layout each of the base stations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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