CN113709754B - Clustering algorithm based wireless broadband communication system station arrangement networking method and system - Google Patents

Abstract

The invention discloses a method and a system for arranging stations and networking a wireless broadband communication system based on a clustering algorithm. Multiple iteration simulation calculations show that the aim of improving the coverage rate can be achieved by each iteration step, and the final algorithm converges to a better result. The method can closely combine the physical significance of the scene to finally obtain a result which is not inferior to that of the traditional site optimization method, but the operation speed is superior to that of the traditional optimization method.

Description

Method and system for wireless broadband communication system deployment and networking based on clustering algorithm

technical field

The invention belongs to the technical field of communication technologies, and in particular relates to a method and system for deploying and networking a wireless broadband communication system based on a clustering algorithm.

Background technique

In traditional mobile communication networks, traditional cellular network structures are mostly used, and the cellular structure is considered to be the best topology covering a two-dimensional plane. However, in special situations such as disaster relief and military operations, the deployment of base stations at this time requires mobility, real-time, high efficiency, and the constraints of terrain factors need to be considered when deploying stations. Therefore, traditional cellular network structure cannot be used to deploy stations. Based on the above problems, it is extremely important to propose an efficient base station deployment method in these special situations.

In fact, site planning of a wireless broadband communication system is an optimization problem. The optimization index is to maximize the communication requirements of the scene, and the signal coverage rate is often used as the optimization index. Considering that in the actual deployment process, not a single base station but multiple base stations are deployed at the same time, so the optimization problem itself is a non-convex optimization problem. For non-convex optimization problems, the academic community does not have a general theory for solving optimal solutions. In engineering, methods such as convexizing non-convex problems and using optimization algorithms such as genetic algorithms are used to solve better solutions. However, the solution process can only obtain a better solution and cannot guarantee that the global optimal solution can be obtained.

Scholars at home and abroad have carried out a lot of work on site planning. The most representative solution is the genetic algorithm. The biggest disadvantage of the genetic algorithm is that it needs to continuously search for the optimization direction in the iteration, which consumes a lot of time and computing resources. In the actual physical background of site planning, this optimization direction is the base station moving direction that can improve the map signal coverage. Moving the base station from a place with strong signal coverage to a place with weak signal coverage can significantly improve the signal coverage of the map. Therefore, moving the base station to an area with weak signal coverage on the map is the direction of base station location optimization. Based on the map signal coverage, the clustering algorithm can be used to calculate the cluster centers with weak signal coverage, and the calculated cluster centers of several weak signal coverage points indicate the direction of base station location optimization. Because the clustering algorithm with lower computational complexity can directly indicate the direction of base station location optimization, it is more efficient than using genetic algorithm. In addition, most of the researches are too ideal for the actual physical environment assumption, and cannot accurately reflect the influence of surrounding terrain on communication quality. Based on the above considerations, it is possible to develop a wireless broadband communication system site layout networking scheme based on a clustering algorithm, which has strong real-time performance and can well reflect the influence of terrain.

Further analysis of the application of the clustering algorithm in this problem can be found that in the process of optimization, the base stations tend to gradually disperse and move towards the weak coverage area. Based on this finding, it is considered to use the clustering algorithm to directly indicate the direction of optimization in the iterative process, which can save a lot of time due to trial and error and waste of computing resources.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a method and system for network deployment of a wireless broadband communication system based on a clustering algorithm, aiming at the deficiencies in the above-mentioned prior art, so as to achieve the purpose of improving the signal coverage rate when the base station site is deployed. .

The present invention adopts following technical scheme:

A method for deploying and networking a wireless broadband communication system based on a clustering algorithm includes the following steps:

S1. Generate a base station layout that meets the requirements by random initialization, and calculate the coverage of the map in the initial base station layout through channel modeling;

S2. According to the coverage of the map obtained in step S1, the k-means clustering algorithm is used to calculate the cluster centers of several uncovered areas, and the cluster centers of the uncovered areas are determined in descending order of the number of points classified to the uncovered points of the cluster center. priority;

S3, traverse each cluster center in turn according to the priority determined in step S2, for traversing to a certain cluster center, calculate the distances from all base stations to the corresponding cluster center in turn to determine the mobile priority, and then traverse all the base stations successively according to the priority;

S4, for traversing a certain base station, make the base station move to the cluster center traversed in step S3, and in the case of satisfying the base station layout conditions, calculate the maximum new coverage rate obtained in the process of moving the base station;

S5, judging the maximum new coverage rate obtained in step S4, if the maximum new coverage rate is greater than the current coverage rate, update the base station location and end the traversal, and one iteration ends;

S6. After step S5, calculate the cluster centers of several new uncovered areas, and start the next iteration until the coverage rate no longer improves. When the expected coverage rate or the maximum number of iterations is reached, the output base station position is the final base station Location;

S7. Calculate the received power of each spatial point on the map according to the final base station position output in step S6, calculate the final signal coverage, and output the position of the deployed base station.

Specifically, step S2 is specifically:

S201, randomly select k coordinates from the coordinates of the input uncovered area as the initial value of the cluster center;

S202. Calculate the Euclidean distance between each point in the uncovered area and the k cluster centers, and select the nearest cluster center for each point as the marker category;

S203, after completing the marked cluster center of each point, recalculate the new center point of each cluster;

S204 , if the sum of the absolute difference between the newly calculated cluster center position and the old cluster center position reaches a threshold or the number of iterations reaches an expected value, stop iteratively outputting the clustering result.

Further, in step S201, the number k of cluster centers of the k-means clustering is the same as the number of base stations.

Further, in step S202, the distance D _ij between the coordinates of the i-th uncovered point relative to the current j-th cluster center is:

Among them, x _i and y _i are the horizontal and vertical coordinates of the i uncovered points, and X _j and Y _j are the horizontal and vertical coordinates of the current jth cluster center.

Further, in step S203, the horizontal and vertical coordinates X'j and _Y'j of the _jth cluster center of the new center point of each cluster are recalculated as:

Among them, x _i and y _i are the abscissa and ordinate of the i-th signal uncovered point classified to the j-th cluster center, and n _j is the number of signal uncovered points classified to the j-th cluster center.

Further, in step S204, if the sum of the absolute difference between the newly calculated cluster center position and the old cluster center position does not reach the threshold, or the number of iterations does not reach the expected value, go to step S202.

Specifically, in step S4, the base station moves to the traversed cluster center with a step size of 1/6 of the distance from the base station to the cluster center. Under the condition that the base station layout conditions are met, the coverage rate after each movement of the base station is calculated, and the maximum coverage as the largest new coverage.

Specifically, in step S6, the condition for terminating the iteration is that the coverage rate of all the cluster centers is not improved after traversing all the cluster centers in a certain iteration, or the specified number of iterations is reached.

Specifically, in step S7, the signal coverage rate obtained from the final location of the base station obtained in step S6 is used as the final signal coverage rate.

Another technical solution of the present invention is a wireless broadband communication system site layout networking system based on a clustering algorithm, comprising:

The calculation module generates a base station layout that meets the requirements by random initialization, and calculates the coverage of the map in the initial base station layout through channel modeling;

The sorting module, according to the coverage of the map obtained by the calculation module, uses the k-means clustering algorithm to calculate the cluster centers of several uncovered areas, and sorts in descending order the number of points classified to the uncovered points of the cluster center to determine the clusters of uncovered areas. heart priority;

The traversal module, according to the priority determined by the sorting module, traverses each cluster center in turn, for traversing to a certain cluster center, calculates the distances from all base stations to the corresponding cluster center in turn to determine the moving priority, and then traverses all the base stations according to the priority;

The mobile module, for traversing a certain base station, makes the base station move to the cluster center traversed by the traversing module, and calculates the maximum new coverage rate obtained in the process of moving the base station under the condition that the base station layout conditions are met;

The judgment module judges the maximum new coverage rate obtained by the mobile module, if the maximum new coverage rate is greater than the current coverage rate, updates the base station location and ends the traversal, and one iteration ends;

Iterative module, after the judgment module is finished, calculate the cluster centers of several new uncovered areas, and start the next iteration until the coverage rate no longer improves. When the expected coverage rate or the maximum number of iterations is reached, the output base station position is the final base station location;

The deployment module calculates the received power of each spatial point of the map according to the final base station position output by the iteration module, calculates the final signal coverage rate, and outputs the position of the deployed base station.

Compared with the prior art, the present invention at least has the following beneficial effects:

Based on the clustering algorithm-based wireless broadband communication system deployment and networking method of the present invention, when analyzing the application of the clustering algorithm in this problem, it can be found that in the process of optimization, the base stations tend to gradually disperse and move toward weak coverage areas. Based on this finding, it is considered to use the clustering algorithm to directly indicate the direction of optimization in the iterative process, which can save a lot of time due to trial and error and waste of computing resources.

Further, based on the commonly used k-means clustering algorithm, the k-means algorithm is used to calculate the cluster center of the signal uncovered area in the map in each iteration calculation, and the signal coverage of the uncovered area can be enhanced based on the base station moving to the uncovered area cluster center. According to the agreed moving rule, only one base station is moved in each iteration calculation to achieve the purpose of improving signal coverage. Iterative simulation calculations show that each iteration can achieve the purpose of improving the coverage, and the final algorithm will converge to a better result. The algorithm can be closely combined with the physical meaning of the scene, and can finally obtain results that are no less than the traditional site optimization algorithm, but the operation speed is ahead of the traditional optimization algorithm, which is the biggest innovation of the present invention.

Further, the number of cluster centers of the k-means clustering algorithm in step S201 cannot be generated by the algorithm itself and needs to be given in advance. Because the base station determines the priority of movement according to the cluster centers, the number of cluster centers should be positively correlated with the number of base stations, so choosing the same number of cluster centers k as the number of base stations in k-means clustering meets the requirements of the algorithm.

Further, in step S202, the clustering algorithm needs to measure the standard of clustering distance, and in this scene, the physical distance D _ij of the i-th uncovered point coordinate relative to the current j-th cluster center is set as the standard of clustering distance. is optimal.

Further, in step S203, the horizontal and vertical coordinates of the jth cluster center of the new center point of each cluster are recalculated, and the calculated new cluster center coordinates can be used for the next iteration.

Further, in step S204, if the sum of the absolute value difference between the newly calculated cluster center position and the old cluster center position does not reach the threshold, or the number of iterations does not reach the expected value, the iterative output result is stopped. The purpose of this setting is to avoid the iterative calculation that is invalid or does not improve the result, and to improve the efficiency of the calculation.

Further, in step S4, the current base station traversed is moved to the corresponding cluster center according to the step size, the new signal coverage after each movement is calculated, and finally the maximum signal coverage is selected as the result of this iteration, so that The advantage of processing is that each iteration can obtain a local optimal solution, and then obtain an approximate global optimal solution after all iterations are completed.

Further, in step S6, the output base station position is the final base station position until the coverage rate no longer improves, reaches the expected coverage rate, or reaches the number of iterations. The purpose of this setting is that these conditions are the termination conditions for terminating the iteration.

Further, the setting of step S7 is to output the result after the iteration is completed, and output the location of the base station and the current signal coverage.

To sum up, the present invention fully considers the actual physical meaning of the problem, instead of blindly using an algorithm with higher design requirements, which can greatly save computing time and also maintain a better result.

The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.

Description of drawings

Figure 1 is a schematic diagram of the scene;

Fig. 2 is the schematic diagram of the algorithm proposed by the method of the present invention at the initial time;

3 is a schematic diagram after one base station location iteration;

Fig. 4 is the schematic diagram after secondary base station position iteration;

Fig. 5 is the flow chart of the present invention;

FIG. 6 is a simulation result diagram of the algorithm proposed by the present invention under different signal receiving sensitivities.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the description of the present invention, it is to be understood that the terms "comprising" and "comprising" indicate the presence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other features, The existence or addition of a whole, step, operation, element, component, and/or a collection thereof.

It should also be understood that the terminology used in the present specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural unless the context clearly dictates otherwise.

It should further be understood that, as used in this specification and the appended claims, the term "and/or" refers to and including any and all possible combinations of one or more of the associated listed items .

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not to scale, some details have been exaggerated for clarity, and some details may have been omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are only exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art should Regions/layers with different shapes, sizes, relative positions can be additionally designed as desired.

The invention provides a wireless broadband communication system deployment and networking method based on a clustering algorithm, which initializes the positions of several base stations that need to be deployed, and simulates the signal coverage of each point in the map through signal modeling. The signal coverage area of is used to cluster the centers of several uncovered areas, and the base station is moved according to a certain priority, and the algorithm is continuously iterated until the coverage rate cannot be improved.

Referring to FIG. 1, the application scenario of the present invention is that several base stations are arranged in a known map information area, and each base station works in different frequency bands to provide telecommunication services for users. Obtaining the optimal base station layout through an algorithm can make the area The signal coverage is optimal.

It is extremely important to choose a suitable channel model in site planning. First, it is necessary to determine the channel model to be used. The link loss of the channel is divided into large-scale fading and small-scale fading. The large-scale average path loss is used to measure the relationship between transmitter and receiver. The average fading of the signal is defined as the difference between the effective transmit power and the average received power. Small-scale fading refers to the fading in the short term, specifically refers to the fast speed of the received signal in the short term when the mobile station moves a small distance. fluctuation.

The method of the present invention selects the more commonly used Okumura-Hata model; the empirical formula is shown in formula (1):

L _m =69.55+26.16lg(f)-13.82lg(h _te )-a(h _re )+[44.9- 6.55lg(h _te )lg(d)] (1)

Among them, f is the carrier frequency (unit: MHZ), h _te is the finite height of the transmitting antenna (unit: m), h _re is the finite height of the receiving antenna (unit: m), and d is the distance between the transmitter and the receiver. Distance (unit: km), a(h _re ) is a mobile antenna correction factor, the value of which depends on the environment.

If the threshold of known signal coverage is γ, n base stations need to be deployed at the same time, and each base station works in different frequency bands without co-channel interference. Specifically, the signal coverage of the mth signal receiving point on the map is judged by the formula (2):

Aiming at the problem to be solved, the present invention optimizes the positions of several base stations through the proposed iterative algorithm based on the clustering algorithm, so as to achieve the optimal signal coverage in the area covered by the base stations under the layout.

The present invention selects the Okumura-Hata model commonly used in the industry as the channel model to calculate the received signal strength of each point in the map. Because the present invention is mainly aimed at base stations operating in different frequency bands, it does not need to consider the co-frequency interference between the base stations. Each point on the map selects the base station with the highest received signal strength to access, and if the received signal strength is higher than the threshold, the signal at that point is considered to be covered.

The method proposed by the present invention is based on the commonly used k-means clustering algorithm, and the k-means algorithm is used to calculate the cluster centers of the signal uncovered areas in the map in each iteration calculation. According to the fact of signal coverage, only one base station is moved in each iteration calculation according to the agreed moving rule to achieve the purpose of improving signal coverage. Iterative simulation calculations show that each iteration can achieve the purpose of improving the coverage, and the final algorithm will converge to a better result. The physical meaning of the scene can be closely combined, and finally the results that are no less than the traditional site optimization algorithm can be obtained, but the operation speed is ahead of the traditional optimization algorithm, which is the biggest innovation of the present invention.

Please refer to FIG. 5 , a method for deploying and networking a wireless broadband communication system based on a clustering algorithm of the present invention obtains the location coordinates of all uncovered areas after obtaining the signal coverage of each point on the map, and uses k-means clustering The algorithm obtains the cluster center of the uncovered area as the optimization direction of the iterative algorithm. Specific steps are as follows:

S1. Randomly initialize and generate a base station layout that meets the requirements, and calculate the coverage of the map through channel modeling according to the initial base station layout;

S2. Use k-means for clustering according to the coverage of the map to calculate the cluster centers of several uncovered areas, and sort these cluster centers in descending order according to the number of uncovered points classified to the cluster center to determine the priority (classification The more points the class gets, the higher the priority);

S201, randomly select k coordinates from the coordinates of the input uncovered area as the initial value of the cluster center, and the number k of the cluster center of the k-means clustering is the same as the number of base stations;

S202. Calculate the Euclidean distance between each point in the uncovered area and the k cluster centers according to formula (3), and select the nearest cluster center for each point as the marker category;

Among them, D _ij represents the distance between the coordinates of the i-th uncovered point relative to the current j-th cluster center, x _i and y _i are the horizontal and vertical coordinates of the i uncovered points, and X _j and Y _j are the current j-th cluster The horizontal and vertical coordinates of the heart.

S203, after completing the marked cluster center of each point, recalculate the new center point of each cluster by formula (4);

Among them, X′ _j and Y′ _j are the abscissa and ordinate coordinates of the updated j-th cluster center, x _i and y _i are the abscissa and ordinate of the i-th signal uncovered point classified to the j-th cluster center, n _j is the number of signal uncovered points classified to the jth cluster center.

S204 , if the sum of the absolute difference between the newly calculated cluster center position and the old cluster center position reaches the threshold or the number of iterations reaches the expected value, stop iteratively outputting the clustering result; otherwise, continue to step S202 .

S3, traverse each cluster center in turn according to the priority, and for traversing a certain cluster center, calculate the distances from all base stations to the cluster center in turn to determine the mobile priority (the closer the distance is, the higher the priority), and then traverse successively according to the priority all base stations;

S4. For traversed to a certain base station, let the base station move to the traversed cluster center with a step size of 1/6 of the distance from the base station to the cluster center, and in the case of satisfying the base station layout conditions that the base stations can communicate with each other, Calculate the coverage rate after each movement of the base station, and take the largest coverage rate as the new maximum coverage rate;

S5, judging the maximum new coverage rate in step S4, if the maximum new coverage rate is greater than the current coverage rate, update the base station location and end the traversal, and this iteration ends;

S6. Then calculate the cluster centers of several new uncovered areas to start the next iteration. The condition for the termination of the iteration is that the coverage rate of all the cluster centers is not improved after traversing all the cluster centers in a certain iteration, or the specified number of iterations is reached;

S7. Finally, according to the final position of the base station, the received power of each spatial point on the map is calculated according to the channel modeling formula, and the received power of each spatial point on the map is calculated based on whether the received power of each spatial point on the map reaches the receiving sensitivity. Coverage, thereby calculating the final signal coverage of the map, and outputting the location where the base station is deployed.

In yet another embodiment of the present invention, a clustering algorithm-based wireless broadband communication system site layout networking system is provided, which can be used to implement the above-mentioned clustering algorithm-based wireless broadband communication system site deployment and networking method, specifically The clustering algorithm-based wireless broadband communication system site layout networking system includes a computing module, a sorting module, a traversal module, a moving module, a judgment module, an iterative module and a deployment module.

Among them, the calculation module randomly initializes and generates a base station layout that meets the requirements, and calculates the coverage of the map in the initial base station layout through channel modeling;

The sorting module, according to the coverage of the map obtained by the calculation module, uses the k-means clustering algorithm to calculate the cluster centers of several uncovered areas, and sorts in descending order the number of points classified to the uncovered points of the cluster center to determine the clusters of uncovered areas. heart priority;

The traversal module, according to the priority determined by the sorting module, traverses each cluster center in turn, for traversing to a certain cluster center, calculates the distances from all base stations to the corresponding cluster center in turn to determine the moving priority, and then traverses all the base stations according to the priority;

The mobile module, for traversing a certain base station, makes the base station move to the cluster center traversed by the traversing module, and calculates the maximum new coverage rate obtained in the process of moving the base station under the condition that the base station layout conditions are met;

The judgment module judges the maximum new coverage rate obtained by the mobile module, if the maximum new coverage rate is greater than the current coverage rate, updates the base station location and ends the traversal, and one iteration ends;

Iterative module, after the judgment module is finished, calculate the cluster centers of several new uncovered areas, and start the next iteration until the coverage rate no longer improves. When the expected coverage rate or the maximum number of iterations is reached, the output base station position is the final base station location;

The deployment module calculates the received power of each spatial point of the map according to the final base station position output by the iteration module, calculates the final signal coverage rate, and outputs the position of the deployed base station.

In yet another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used for storing a computer program, the computer program includes program instructions, and the processor is used for executing the computer Program instructions stored in the storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gates Field-Programmable GateArray (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., are the computing core and control core of the terminal, and are suitable for implementing one or more instructions. Loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the method for deploying and networking a wireless broadband communication system based on a clustering algorithm, including:

Random initialization generates a base station layout that meets the requirements, and calculates the coverage of the map in the initial base station layout through channel modeling; Determine the cluster center priority of the uncovered area according to the descending order of the points classified to the uncovered points of the cluster center; traverse each cluster center in turn according to the determined priority, and calculate all base stations to the corresponding cluster in turn for a certain cluster center The distance from the center determines the mobile priority, and then traverses all the base stations according to the priority; for traversing a certain base station, the base station moves to the traversed cluster center, and when the base station layout conditions are met, the calculation of the mobile base station is obtained. the maximum new coverage rate; judge the obtained maximum new coverage rate, if the maximum new coverage rate is greater than the current coverage rate, update the base station location and end the traversal, one iteration ends; calculate several new clusters of uncovered areas Start the next iteration until the coverage no longer improves. When the expected coverage is reached or the maximum number of iterations is reached, the output base station position is the final base station position; the received power of each spatial point on the map is calculated according to the output final base station position. , calculate the final signal coverage, and output the location where the base station is deployed.

In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), where the computer-readable storage medium is a memory device in a terminal device for storing programs and data . It can be understood that, the computer-readable storage medium here may include both a built-in storage medium in the terminal device, and certainly also an extended storage medium supported by the terminal device. The computer-readable storage medium provides storage space in which the operating system of the terminal is stored. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.

One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for deploying and networking of wireless broadband communication systems based on the clustering algorithm in the above-mentioned embodiments; the computer-readable storage medium One or more instructions in is loaded by the processor and performs the following steps:

Random initialization generates a base station layout that meets the requirements, and calculates the coverage of the map in the initial base station layout through channel modeling; Determine the cluster center priority of the uncovered area according to the descending order of the points classified to the uncovered points of the cluster center; traverse each cluster center in turn according to the determined priority, and calculate all base stations to the corresponding cluster in turn for a certain cluster center The distance from the center determines the mobile priority, and then traverses all the base stations according to the priority; for traversing a certain base station, the base station moves to the traversed cluster center, and when the base station layout conditions are met, the calculation of the mobile base station is obtained. the maximum new coverage rate; judge the obtained maximum new coverage rate, if the maximum new coverage rate is greater than the current coverage rate, update the base station location and end the traversal, one iteration ends; calculate several new clusters of uncovered areas Start the next iteration until the coverage no longer improves. When the expected coverage is reached or the maximum number of iterations is reached, the output base station position is the final base station position; the received power of each spatial point on the map is calculated according to the output final base station position. , calculate the final signal coverage, and output the location where the base station is deployed.

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Please refer to Figure 2, randomly initialize the station layout of several bases, use the k-means clustering algorithm to cluster the areas not covered by the signal to obtain the cluster center points, and determine the priority of the cluster center according to the descending order of the number of various types in the clustering result (that is, the higher the number of clusters, the higher the priority). For each cluster center, the priority of moving the base station for the cluster center is determined in ascending order according to the distance of each base station from the cluster center, and the base station is moved in a certain step according to the priority (that is, the closer the distance, the higher the priority). In the initial layout, the cluster center and the mobile priority of each base station are determined. First, determine that the moving direction with the highest priority is cluster center 1, and determine that the base station with the highest moving priority is BS4, where the arrow direction is the moving direction of BS4. New coverage for each move. The calculation finds that the maximum new coverage rate in the moving process is higher than the original coverage rate, and the position of BS4 is updated at this time.

Referring to FIG. 3 , the position and priority of each cluster center and the priority of the corresponding base station are updated according to the updated new position. At this time, the cluster center point with the highest priority has changed its position, but moving the base station BS4 with the highest priority does not help to improve the coverage, so the position of the base station BS4 is not changed to choose to move the base station BS3 with the second priority. By moving the base station BS3 according to the rules in the direction of the arrow, it can be found that the coverage rate can be improved, and the position of the base station BS3 with the maximum coverage rate can be recorded at the same time and the position of the base station BS3 is updated.

Referring to FIG. 4 , the above algorithm is repeated according to the updated positions of each base station. The coverage cannot be improved until all base stations are traversed, and the convergence condition of the algorithm is reached. The flowchart of the algorithm is shown in Figure 5.

According to the clustering algorithm mentioned above, MATLAB R2020a is used for computer simulation simulation. The computer configuration is: processor Intel i5-8250U 1.60GHz, memory 8GB. The simulation environment is as follows:

Table 1 Description of simulation parameters

Please refer to Figure 6, which shows the simulation results under different signal receiving sensitivities. It can be found from the results that with the increase of the receiving sensitivity, the map signal coverage gradually decreases. This is because the increase of the receiving sensitivity will reduce the coverage of the base station, and the map signal coverage will decrease when other conditions remain unchanged. . In addition, it can be found that the running time of the algorithm has a certain volatility, which is because the convergence result of the algorithm depends on the initial random state and thus has a certain randomness.

To sum up, the present invention is a method and system for wireless broadband communication system deployment and networking based on a clustering algorithm, which fully considers the actual physical meaning of the problem, instead of blindly using algorithms with higher design requirements, which greatly saves computation. Time can also maintain a better result.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

The above content is only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention all fall within the scope of the claims of the present invention. within the scope of protection.

Claims (7)

1. a wireless broadband communication system based on a clustering algorithm, is characterized in that, comprises the following steps: S1. Generate a base station layout that meets the requirements by random initialization, and calculate the coverage of the map in the initial base station layout through channel modeling; S2. According to the coverage of the map obtained in step S1, the k-means clustering algorithm is used to calculate the cluster centers of several uncovered areas, and the cluster centers of the uncovered areas are determined in descending order of the number of points classified to the uncovered points of the cluster center. priority, specifically: S201, randomly select k coordinates from the coordinates of the input uncovered area as the initial value of the cluster center; S202. Calculate the Euclidean distance between each point in the uncovered area and the k cluster centers, and select the nearest cluster center for each point as the marker category; S203: After completing the marking of the cluster centers of each point, recalculate the new center point of each cluster, and recalculate the abscissa X′ _j and Y of the jth cluster center of the new center point of each cluster ' _j is:

Among them, x _i and y _i are the abscissa and vertical coordinates of the i-th signal uncovered point classified to the j-th cluster center, and n _j is the number of signal uncovered points classified to the j-th cluster center; S204, if the sum of the absolute value difference between the newly calculated cluster center position and the old cluster center position reaches the threshold or the number of iterations reaches the expected value, stop iterative output of the clustering result, if the newly calculated cluster center position and the old cluster center position The sum of the absolute value difference does not reach the threshold, or the number of iterations does not reach the expected value, continue to step S202; S3, traverse each cluster center in turn according to the priority determined in step S2, for traversing to a certain cluster center, calculate the distances from all base stations to the corresponding cluster center in turn to determine the mobile priority, and then traverse all the base stations successively according to the priority; S4, for traversing a certain base station, make the base station move to the cluster center traversed in step S3, and in the case of satisfying the base station layout conditions, calculate the maximum new coverage rate obtained in the process of moving the base station; S5, judging the maximum new coverage rate obtained in step S4, if the maximum new coverage rate is greater than the current coverage rate, update the base station location and end the traversal, and one iteration ends; S6. After step S5, calculate the cluster centers of several new uncovered areas, and start the next iteration until the coverage rate no longer improves. When the expected coverage rate or the maximum number of iterations is reached, the output base station position is the final base station Location; S7. Calculate the received power of each spatial point on the map according to the final base station position output in step S6, calculate the final signal coverage, and output the position of the deployed base station. 2 . The method according to claim 1 , wherein, in step S201 , the number k of cluster centers in k-means clustering is the same as the number of base stations. 3 . 3. The method according to claim 1, wherein, in step S202, the distance D _ij of the i-th uncovered point coordinate relative to the current j-th cluster center is:

Among them, x _i and y _i are the horizontal and vertical coordinates of the i uncovered points, and X _j and Y _j are the horizontal and vertical coordinates of the current jth cluster center. 4. The method according to claim 1, wherein in step S4, the base station moves to the traversed cluster center with 1/6 of the distance from the base station to the cluster center as a step, and under the condition that the base station layout condition is satisfied, Calculate the coverage after each movement of the base station, and take the largest coverage as the largest new coverage. 5 . The method according to claim 1 , wherein, in step S6 , the condition for terminating the iteration is that the coverage rate of all cluster centers is not improved after traversing all the cluster centers in a certain iteration, or the specified number of iterations is reached. 6 . 6 . The method according to claim 1 , wherein in step S7 , the signal coverage rate obtained from the final location of the base station obtained in step S6 is used as the final signal coverage rate. 7 . 7. A wireless broadband communication system site layout networking system based on a clustering algorithm is characterized in that, comprising: The calculation module generates a base station layout that meets the requirements by random initialization, and calculates the coverage of the map in the initial base station layout through channel modeling; The sorting module, according to the coverage of the map obtained by the calculation module, uses the k-means clustering algorithm to calculate the cluster centers of several uncovered areas, and sorts in descending order the number of points classified to the uncovered points of the cluster center to determine the clusters of uncovered areas. Heart priority, specifically: Randomly select k coordinates from the input coordinates of the uncovered area as the initial value of the cluster center; calculate the Euclidean distance between each point in the uncovered area and the k cluster centers, and select the nearest cluster center as a marker for each point Category; after completing the marked cluster center of each point, recalculate the new center point of each cluster, and recalculate the abscissa X′ _j and Y of the jth cluster center of the new center point of each cluster ' _j is:

Among them, x _i and y _i are the abscissa and vertical coordinates of the i-th signal uncovered point classified to the j-th cluster center, and n _j is the number of signal uncovered points classified to the j-th cluster center; If the sum of the absolute difference between the newly calculated cluster center position and the old cluster center position reaches the threshold or the number of iterations reaches the expected value, the iterative output of the clustering results is stopped. If the absolute difference between the newly calculated cluster center position and the old cluster center position If the sum of the value differences does not reach the threshold, or the number of iterations does not reach the expected value, continue to calculate the Euclidean distance between each point in the uncovered area and the k cluster centers; The traversal module, according to the priority determined by the sorting module, traverses each cluster center in turn, for traversing to a certain cluster center, calculates the distances from all base stations to the corresponding cluster center in turn to determine the moving priority, and then traverses all the base stations according to the priority; The mobile module, for traversing a certain base station, makes the base station move to the cluster center traversed by the traversing module, and calculates the maximum new coverage rate obtained in the process of moving the base station under the condition that the base station layout conditions are met; The judgment module judges the maximum new coverage rate obtained by the mobile module, if the maximum new coverage rate is greater than the current coverage rate, updates the base station location and ends the traversal, and one iteration ends; Iterative module, after the judgment module is finished, calculate the cluster centers of several new uncovered areas, and start the next iteration until the coverage rate no longer improves. When the expected coverage rate or the maximum number of iterations is reached, the output base station position is the final base station location; The deployment module calculates the received power of each spatial point of the map according to the final base station position output by the iteration module, calculates the final signal coverage rate, and outputs the position of the deployed base station.

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