A Cell Dynamic Networking Planning Method for Distributed Base Station Systems

Abstract

This application relates to the technical field of cell dynamic networking planning, and specifically relates to a cell dynamic networking planning method for a distributed base station system. The method includes: in the distributed base station system, each macro base station and each micro base station are all denoted as base stations; the identity codes of users who communicate with any base station within a preset time period are used to form the user group set of the any base station, and the user trajectory weights of each user at each moment are determined; the base station overlap degree between any two base stations is determined; based on the possible situations where each micro base station is divided into the affiliated cells of each macro base station, individual vectors are randomly generated; the adaptability of each individual vector is determined; based on the adaptability of each individual vector, a genetic algorithm is used to obtain the optimal individual vector, and cell dynamic networking planning is performed on the distributed base station system. This application improves the rationality of cell dynamic networking planning and improves the communication quality.

Description

Technical Field

[0001] This application relates to the technical field of cell dynamic networking planning, and specifically relates to a cell dynamic networking planning method for a distributed base station system. Background Art

[0002] Existing network communication systems are composed of multiple base stations, and users complete Internet access by communicating with the base stations. In core areas such as cities, where buildings are dense and the traffic volume is large, the existing base station systems are difficult to provide sufficient communication services. Therefore, micro base stations are added to the existing base station systems, and a distributed base station system combining macro base stations and micro base stations appears.

[0003] In a distributed base station system, micro base stations are responsible for communicating between users and macro base stations, bypassing building obstructions and increasing the service range of macro base stations. Therefore, in the existing distributed base station systems, multiple cells are formed with macro base stations as the main body, and each cell is composed of multiple micro base stations to complete communication. Among them, determining which macro base station a micro base station should be managed by constitutes the problem of cell dynamic networking planning.

[0004] In the existing cell dynamic networking planning methods, fixed cell division is usually adopted. When the distribution of cell users is uneven or the number of users changes dynamically, the fixed divided cells cannot adapt to the actual communication needs, which may cause network congestion in some areas, while the network resource utilization rate in other areas is low, resulting in low communication quality. Summary of the Invention

[0005] In order to solve the above technical problems, this application provides a cell dynamic networking planning method for a distributed base station system to solve the existing problems.

[0006] A cell dynamic networking planning method for a distributed base station system of this application adopts the following technical solutions:

[0007] An embodiment of this application provides a cell dynamic networking planning method for a distributed base station system, and this method includes the following steps:

[0008] In a distributed base station system, each macro base station and each micro base station are all recorded as base stations; obtain the geographical coordinates of each base station; form a user group set of any base station by encoding the identities of users who have communicated with the any base station within a preset time period, and obtain a preset number of user group sets of each base station;

[0009] Determine the reciprocal of the number of base stations where each user has communicated within the preset time period before each moment as the user trajectory weight of each user at each moment; analyze the user overlap degree between any two user group sets belonging to different base stations, and combine the user trajectory weight to obtain the user overlap rate between the any two user group sets.

[0010] Analyze the time gap corresponding to any two user group sets in time series, and determine the base station overlap degree between any two base stations based on the user coincidence rate and the time gap of all any two user group sets of any two base stations.

[0011] Based on the possible situations of each micro base station being assigned to the cells of each macro base station, randomly generate each individual vector; analyze the base station overlap degree between the two base stations corresponding to each element in each individual vector, and the distance between the geographical coordinates of the two base stations corresponding to each element, and determine the adaptability of each individual vector.

[0012] Based on the adaptability of each individual vector, use the genetic algorithm to obtain the optimal individual vector and perform cell dynamic networking planning for the distributed base station system.

[0013] In one embodiment, the determination process of the user coincidence rate is as follows:

[0014] Denote any user group set of any one base station as Y1, and denote any user group set of any one base station among the base stations other than the any one base station in the distributed base station system as Y2;

[0015] Calculate the sum value of the user trajectory weights of all users in the intersection of Y1 and Y2, denoted as the first sum value, calculate the sum value of the user trajectory weights of all users in the union of Y1 and Y2, denoted as the second sum value, and combine the first sum value and the second sum value to obtain the user coincidence rate.

[0016] In one embodiment, the user coincidence rate is the ratio of the first sum value to the second sum value.

[0017] In one embodiment, the determination process of the base station overlap degree is as follows:

[0018] For the user coincidence rate between all any two user group sets of any two base stations, divide the user coincidence rates corresponding to the same time gap into the same category, arrange all the divided categories in ascending order of the time gap, obtain the normalized value of the reciprocal of the serial numbers of all categories after arrangement, calculate the average value of all user coincidence rates of all categories, and determine the base station overlap degree based on the normalized value and the average value.

[0019] In one embodiment, the base station overlap degree is the cumulative sum of the products of the normalized values and the average values of all categories of any two base stations.

[0020] In one embodiment, the number of elements in the individual vector is equal to the number of micro base stations in the distributed base station system. If the m-th element in the individual vector is n, it represents that the m-th micro base station is assigned to the cell to which the macro base station n belongs.

[0021] In one embodiment, the determination of the adaptability includes:

[0022] For any individual vector, calculate the base station overlap degree between the two base stations corresponding to each element according to its allocation scheme. If both of the two base stations are micro base stations and the corresponding element values in the said individual vector are the same, then exclude the corresponding base station overlap degree; if the two base stations are a micro base station and a macro base station respectively, denoted as micro base station A and macro base station B, and the element value corresponding to micro base station A in the said individual vector is macro base station B, then exclude the corresponding base station overlap degree; if both of the two base stations are macro base stations, then exclude the corresponding base station overlap degree;

[0023] Calculate the sum value of all the remaining base station overlap degrees after excluding the base station overlap degrees from the said individual vector, denoted as the adjacent cell interference eigenvalue of the said individual vector, and combine it with the distance between the geographical coordinates of the two base stations corresponding to each element in the said individual vector to obtain the adaptability of the said individual vector.

[0024] In one embodiment, calculate the average value of the distances between the geographical coordinates of the two base stations corresponding to all the elements in the said individual vector, and the adaptability of the said individual vector is the product of the adjacent cell interference eigenvalue and the average value.

[0025] In one embodiment, the optimal individual vector is the individual vector with the minimum adaptability.

[0026] This application has at least the following beneficial effects:

[0027] In this application, each macro base station and each micro base station included in the distributed base station system are denoted as base stations; the geographical coordinates of each base station are obtained; the identity codes of users who have communicated with any base station within a preset time period are used to form the user group set of the any base station, and a preset number of user group sets of each base station are obtained; based on the number of base stations with which each user has communicated within the preset time period before each moment, the user trajectory weight of each user at each moment is determined; the user trajectory weight reflects the position movement of the user through the frequency of the user switching base stations, and a higher weight is given to users with a smaller activity range to reduce the impact of frequent base station switching caused by the rapid movement of the user on the adjacent cell interference judgment; the user coincidence degree between any two user group sets belonging to different base stations is analyzed, and combined with the user trajectory weight, the user coincidence rate between the any two user group sets is obtained; the user coincidence rate characterizes the overlapping situation of users managed between two base stations, and the signal overlapping situation between two base stations is evaluated through the number of identical users managed by the two base stations, and the adjacent cell interference estimation is completed. Compared with the existing method for judging adjacent cell interference by geographical location, this application can avoid misjudging areas with dense buildings or uninhabited areas as signal overlapping areas, thereby reducing the error of adjacent cell interference judgment; the time gap corresponding to any two user group sets in time series is analyzed, and based on the user coincidence rate and the time gap of all any two user group sets of any two base stations, the base station overlapping degree between the any two base stations is determined; the base station overlapping degree further reflects the user overlapping situation between two base stations from the time dimension of the user coincidence rate, which helps to more accurately judge the adjacent cell interference situation between base stations; based on the possible situations of each micro base station being divided into the sub-cells of each macro base station, individual vectors are randomly generated; the base station overlapping degree between any two base stations in each individual vector and the distance between the geographical coordinates of any two base stations are analyzed to determine the adaptability of each individual vector; the adaptability comprehensively reflects the adjacent cell interference situation and communication delay situation between base stations; based on the adaptability of each individual vector, the genetic algorithm is used to obtain the optimal individual vector for the cell dynamic networking planning of the distributed base station system; the final planning scheme can adapt to the real-time distribution of users, making the planning scheme more flexible and efficient, reducing the impact of adjacent cell interference and communication delay, improving the rationality of the cell dynamic networking planning, and further improving the communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 The flowchart of steps of a cell dynamic networking planning method provided for this application and oriented to a distributed base station system;

[0030] Figure 2 The distribution schematic diagram of macro base stations and micro base stations;

[0031] Figure 3 The flowchart for determining the adaptability degree. Specific implementation manners

[0032] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to elaborate in detail on a cell dynamic networking planning method provided according to this application, its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0034] The following specifically describes the specific solution of a cell dynamic networking planning method provided by this application with reference to the accompanying drawings.

[0035] A cell dynamic networking planning method provided by an embodiment of this application. Specifically, a cell dynamic networking planning method is provided as follows. Please refer to Figure 1 , and this method includes the following steps:

[0036] Step S001: In the distributed base station system, each macro base station and each micro base station are all denoted as base stations; obtain the geographical coordinates of each base station; form the user group set of any base station by the identity codes of the users who communicate with the any base station within a preset time period, and obtain the preset number of user group sets of each base station.

[0037] For the distributed base station system, common network quality influencing factors are the communication delay between base stations and the co-channel interference between base stations. The distributed base station system includes each macro base station and each micro base station. The distribution schematic diagram of macro base stations and micro base stations is as Figure 2 shown. Figure 2There is 1 macro base station and 5 micro base stations. There is communication between the micro base stations and the macro base station. The ellipse surrounding the micro base station represents the signal coverage range of the micro base station. If there is an area that is an overlapping area of the signal coverage ranges of multiple micro base stations, when these micro base stations are not managed by the same macro base station, due to the lack of coordination of the macro base station, users in this area are prone to inter-cell interference. And the communication delay is because the micro base stations in the same cell need to send the collected information to the macro base station, and communication delay will occur during this process. Generally, the shorter the distance between the micro base station and the macro base station, the smaller the communication delay.

[0038] For inter-cell interference, it usually occurs between two different cells. When a user is located at the boundary position between two cells, since the users at the boundary position will be affected by the signals from the base stations of two different cells, these signals will interfere with each other and affect the communication quality. If the two different base stations are from the same cell, then these two base stations are controlled by the same macro base station, and the control system of the macro base station will perform signal coordinated transmission for these two cells to reduce the influence of signal interference.

[0039] Therefore, when dynamically dividing cells, it is necessary to improve the communication quality from two aspects: communication delay and inter-cell interference.

[0040] In the distributed base station system of this embodiment, there are a total of K = 100 base stations, among which there are a total of N = 9 macro base stations, corresponding to 9 cells, and the remaining M = 91 are micro base stations. Both the macro base stations and the micro base stations are denoted as base stations. Obtain the geographical coordinates of each base station. Among them, the number of base stations in the distributed base station system, as well as the number of macro base stations and the number of micro base stations, can be set by the implementer according to the actual situation, and this embodiment does not make any restrictions here.

[0041] Taking the kth base station in the distributed base station system as an example, the identity codes of the users who have communicated with the kth base station within a preset time period are used to form a user group set of the kth base station. The preset time period in this embodiment is 1 minute, and the implementer can set it according to the actual situation, and this embodiment does not make any restrictions here; within one hour before each moment, obtain the user group set of each minute of the kth base station. Therefore, for the kth base station at each moment, this embodiment obtains T = 60 user group sets, corresponding to 60 minutes in the past hour at each moment.

[0042] Using the same method as the kth base station to obtain the user group set, obtain the T user group sets of each base station in the distributed base station system at each moment, where the number of user group sets can be set by the implementer. Denote the user group sets of each base station at each moment as base station monitoring data.

[0043] Step S002: Determine the user trajectory weights of each user at each moment based on the number of base stations with which each user communicates within the preset duration before each moment; analyze the user overlap degree between any two user group sets belonging to different base stations, and combine the user trajectory weights to obtain the user overlap rate between the any two user group sets.

[0044] To improve the communication quality provided by different micro base stations in a distributed base station system, in this embodiment, cell division is performed according to the real-time collected base station monitoring data.

[0045] In this embodiment, cell division is performed on an hourly basis. The specific method is as follows: Every hour, the micro base stations need to be re-divided according to the user information in the past hour.

[0046] It should be noted that for newly deployed micro base stations, since their working time is less than one hour, according to the Euclidean distance between their geographical coordinates and N macro base stations, the cell where the nearest macro base station is located is selected, and the new micro base station is added; for newly deployed macro base stations, since their working time is less than one hour, they will work as separate base stations within this hour and be added to the distributed base station system in the next hour.

[0047] When the existing method judges the co-channel interference of adjacent cells, it usually evaluates the co-channel interference situation of adjacent cells based on the distance between the geographical locations of the base stations. In the actual usage area of micro base stations, there are usually relatively dense buildings. There may be no actual users in the signal coverage overlapping area of base stations with relatively close geographical locations, and thus there may be no co-channel interference situation. Therefore, the existing co-channel interference estimation method has the problem of inaccurate judgment, which in turn leads to unreasonable cell division and reduces the communication quality. In response to this, in this embodiment, the co-channel interference situation between base stations is judged from the historical interaction data between the base stations and users.

[0048] First of all, in this embodiment, the co-channel interference situation between base stations is evaluated by the handover of users between different base stations. If a user switches back and forth between a few different base stations in a short time, the reason for the user to switch base stations may be that the area where the user is located is the signal overlapping area between these different base stations. If a user switches between a relatively large number of different base stations in a short time, the reason for the user to switch base stations may be the base station handover phenomenon caused by the user's own rapid movement in a short time.

[0049] Based on the above analysis, in this embodiment, the user trajectory weights of each user at each moment are calculated to characterize whether the user is moving rapidly at each moment. Specifically: at time t, through the user group sets of the K base stations at this time, the number of base stations with which the r-th user establishes communication connections within the previous minute at time t is obtained, denoted as S, and the reciprocal of S is taken as the user trajectory weight of the r-th user at time t. Using the same calculation method as the user trajectory weight of the r-th user at time t, the user trajectory weights of each user at each moment are obtained.

[0050] It should be understood that the larger the user trajectory weight, the fewer the number of base stations switched by the r-th user in a short period of time. At this time, the activity range of the r-th user in a short period of time is smaller, and the base station switching phenomenon of the r-th user is more likely to be caused by the user being in the signal overlapping area of different base stations, rather than the user's own rapid movement; furthermore, at time t, the r-th user can more reflect the existence of a signal overlapping area between the different base stations with which it communicates. Furthermore, in the subsequent adjacent area interference assessment, the larger the user trajectory weight of the r-th user, the greater the weight assigned to the r-th user to achieve a more accurate adjacent area interference assessment effect.

[0051] For the current moment, taking the -th base station and the -th base station in the distributed base station system as an example, the -th base station and the -th base station both have T = 60 user group sets. Select an arbitrary user group set from the T user group sets of the -th base station, denoted as Y1, and select an arbitrary user group set from the T user group sets of the -th base station, denoted as Y2.

[0052] The intersection and union of the user group set Y1 and the user group set Y2 are respectively obtained, the sum value of the user trajectory weights of all users in the intersection is calculated, denoted as the first sum value, the sum value of the user trajectory weights of all users in the union is calculated, denoted as the second sum value, and the ratio of the first sum value to the second sum value is taken as the user coincidence rate between the user group set Y1 and the user group set Y2.

[0053] It should be understood that both the first sum value and the second sum value are equivalent to the number of users statistically weighted by the user trajectory weight. Therefore, the first sum value can be regarded as the common number of users in the two user group sets, the second sum value can be regarded as the total number of users in the two user group sets, and the user trajectory weight assigns weights to the users in the statistical process. The larger the weight of the user, the smaller the moving distance in a short period of time, and the more important the analysis of this part of the users is when judging whether there is adjacent area interference.

[0054] The user overlap rate is the ratio of the first sum value to the second sum value. The larger the value, the more users switch back and forth between the th base station and the th base station. Then, the more likely there is a signal coverage overlap area between the th base station and the th base station, and the more likely they are two base stations with adjacent cell interference. When dividing cells in the future, the th base station and the th base station should be divided into the same cell as much as possible to reduce adjacent cell interference. Compared with the existing judgment of adjacent cell interference based on geographical location distance, the user overlap rate in this application reflects the degree of overlap of the communication areas of two base stations through the historical communication behavior of users, and can avoid regarding locations such as building-dense areas and unoccupied areas as signal overlap areas, resulting in errors in adjacent cell interference judgment.

[0055] Step S003: Analyze the time gap corresponding to any two user group sets in time series, and determine the base station overlap degree between any two base stations based on the user overlap rate and the time gap of all any two user group sets of any two base stations.

[0056] For the th base station and the th base station, using the above calculation method of the user overlap rate, a total of user overlap rates can be obtained. Among them, the user overlap rate is obtained based on the comparison of user group sets, and different user group sets correspond to different times. Therefore, in this embodiment, the base station overlap degree between two base stations is further calculated from the time dimension of the user overlap rate, which helps to judge the adjacent cell interference situation between base stations more accurately.

[0057] First, for the p-th user overlap rate between the th base station and the th base station, calculate the time difference between the two user group sets corresponding to the p-th user overlap rate, that is, the time gap. For example, one of the user group sets corresponding to the p-th user overlap rate is the third minute within the previous hour of the current moment of the th base station, and the other user group set is the 10th minute within the previous hour of the current moment of the th base station. Then, the time difference between the two user group sets corresponding to the p-th user overlap rate is equal to 7. Generally, the smaller the time difference between two user group sets, the shorter the time it takes for users to switch base stations between the two base stations, and the more likely there is a cross area of signal coverage between the two base stations.

[0058] Furthermore, for the th base station and the base stations, where the time difference between two user group sets ranges from 0 to 59, and according to the principle of classifying into one category with the same time difference, the user coincidence rate is divided into T = 60 categories, and each category is numbered. Specifically, the number of each category is the sum of the time difference corresponding to each category and the value 1.

[0059] Finally, calculate the mean value of the user coincidence rate of all users in each category, and calculate the normalized value of the reciprocal of the number of each category. Specifically, calculate the cumulative sum of the reciprocals of the numbers of T categories, and calculate the ratio of the reciprocal of the number of each category to the cumulative sum as the normalized value of the reciprocal of the number of each category, denoted as the time weight of each category. Among them, the smaller the time difference of the i-th category, the greater the time weight, indicating that the time taken for the user to switch between the th base station and the th base station is shorter, and at this time, the user coincidence rate can better illustrate the th base station and the th base station have an overlapping signal coverage area.

[0060] Therefore, using the time weight as the weight, perform a weighted sum of the mean values of the user coincidence rates of all users in each of the T categories as the th base station and the th base station's base station overlap degree, which reflects the base station overlap situation between the th base station and the th base station. The greater the base station overlap degree, the more likely it is that the th base station and the th base station have an overlapping signal coverage area, and the more serious the phenomenon of adjacent cell interference between the th base station and the th base station.

[0061] Adopt the same calculation method as the base station overlap degree between the th base station and the th base station to obtain the base station overlap degree between any two base stations in the distributed base station system.

[0062] Step S004: Based on the possible situations of each micro base station being assigned to the cells of each macro base station, randomly generate each individual vector; analyze the base station overlap degree between the two base stations corresponding to each element in each individual vector, and the distance between the geographical coordinates of the two base stations corresponding to each element, and determine the adaptability of each individual vector.

[0063] For the cell network planning, in this embodiment, first generate a variety of random cell division schemes, and combine the base station overlap degree and communication delay to screen out the optimal cell division scheme. The specific process is as follows:

[0064] First, randomly generate individual vectors of length M, where the elements in the individual vectors are integers from 1 to N. For example, if the m-th element in the vector is n, it means that the m-th micro base station is assigned to the cell belonging to macro base station n. That is, the value range of the elements in the individual vector, the integers from 1 to N respectively correspond to N macro base stations, and the positions of the elements in the individual vector correspond to M micro base stations.

[0065] In this embodiment , the implementer can set it according to the actual situation, and this embodiment does not limit it.

[0066] Secondly, calculate the fitness for each individual vector. The calculation method for the fitness of the c-th individual vector is as follows:

[0067] For the K base stations in the distributed base station system, calculate the base station overlap degree between any two base stations. Further, screen all the calculated base station overlap degrees. The screening method is as follows: Each base station overlap degree corresponds to two base stations, and analyze these two base stations:

[0068] In the first case, if these two base stations are micro base stations, find the corresponding element values of these two micro base stations in the c-th individual vector. If the element values are the same, it means that according to the cell division scheme of the c-th individual vector, these two base stations are assigned to the same cell. At this time, eliminate the base station overlap degree between these two base stations; otherwise, these two base stations are assigned to different cells, and the base station overlap degree between these two base stations is retained;

[0069] In the second case, if one of these two base stations is a micro base station and the other is a macro base station, denoted as micro base station A and macro base station B, then find the element corresponding to micro base station A in the c-th individual vector. If the element value is the same as the element value of macro base station B, it means that according to the cell division scheme of the c-th individual vector, micro base station A and macro base station B are assigned to the same cell. At this time, eliminate the base station overlap degree between micro base station A and macro base station B; otherwise, micro base station A and macro base station B are assigned to different cells, and retain the base station overlap degree between micro base station A and macro base station B;

[0070] In the third case, if these two base stations are both macro base stations, then these two base stations cannot be assigned to the same cell and have no impact on cell division. Eliminate the base station overlap degree between these two base stations.

[0071] In the first and second cases, when two base stations are assigned to the same cell, the two base stations communicate with users under the coordination of the cell internal control system. At this time, there will be no co-channel interference in the overlapping area of the communication signals of the two base stations. Therefore, when screening cell division by co-channel interference, it is not necessary to consider the overlapping situation of the signal coverage areas of the two base stations. Furthermore, when calculating the adaptability, the base station overlap degree between the two base stations is excluded.

[0072] Using the analysis methods of the above three cases, all calculated base station overlap degrees are screened in sequence. Finally, the sum value of the retained base station overlap degrees is used as the co-channel interference eigenvalue of the c-th individual vector. The larger the co-channel interference eigenvalue, the more serious the co-channel interference phenomenon of the entire distributed base station system under the cell division scheme of the c-th individual vector, and the less the cell division scheme of the c-th individual vector should be selected.

[0073] Meanwhile, if the value of the m-th element in the c-th individual vector is n, it means that the m-th micro base station and the macro base station n are assigned to the same cell. Generally, the farther the distance between them, the greater the communication delay. Therefore, the Euclidean distance between the geographical coordinates of the m-th micro base station and the macro base station n is calculated as the communication delay corresponding to the m-th element value. Then, each element of the c-th individual vector is traversed, and the average value of the communication delays of all elements in the c-th individual vector is calculated as the overall communication delay of the c-th individual vector. The larger the overall communication delay, the greater the base station communication delay caused by the cell division scheme of the c-th individual vector, and the less the cell division scheme of the c-th individual vector should be selected.

[0074] Finally, the product of the overall communication delay of the c-th individual vector and the co-channel interference eigenvalue of the c-th individual vector is denoted as the adaptability of the c-th individual vector. The larger the adaptability, the greater the co-channel interference and communication delay caused by the cell division scheme of the c-th individual vector, and the less the c-th individual vector should be retained. The flowchart for determining the adaptability is as Figure 3 shown.

[0075] Using the same calculation method as the adaptability of the c-th individual vector, the adaptabilities of randomly obtained individual vectors can be obtained.

[0076] Step S005, based on the adaptabilities of individual vectors, use the genetic algorithm to obtain the optimal individual vector for dynamic cell networking planning of the distributed base station system.

[0077] This embodiment The optimization process of the individual vectors is as follows: Retain the individual vectors with the smallest adaptability as elite individual vectors; in this implementation, the number of elite individuals is Then, Crossing and mutation operations are performed on the elite individual vectors. In this embodiment, the crossing rate is set to 0.25, the mutation rate is set to 0.05, and the population size is set to , and the crossing and mutation operations are the content of the genetic algorithm, which is a well-known existing technology; the final output is individual vectors;

[0078] Take the latest obtained individual vectors as the individual vectors for this round of loop, calculate the fitness of each of the latest individual vectors, and loop the optimization process until the loop reaches times. The individual vectors obtained in the last loop, and select the individual vector with the minimum fitness as the final cell division vector. Among them, in this embodiment , , The values of can be set by the implementer according to the actual situation, and are not limited in this embodiment.

[0079] Finally, the length of the obtained cell division vector is M. Among them, if the value of the m-th element is n, it means that the m-th micro base station is divided into the cell to which the macro base station n belongs, and the cell dynamic networking planning of the distributed base station system is completed.

[0080] It should be noted that: the above-mentioned order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be beneficial.

[0081] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0082] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for dynamic cell networking planning for a distributed base station system, characterized in that: The method comprises the following steps: The distributed base station system includes macro base stations and micro base stations, all of which are recorded as base stations; the geographical coordinates of each base station are obtained; the identity codes of users who have communicated with any base station within a preset time period are encoded to form a user group set of any base station, and a preset number of user group sets of each base station are obtained; The reciprocal of the number of base stations with which each user communicates within the preset time period before each moment is determined as the user trajectory weight of each user at each moment; the degree of user overlap between any two user groups belonging to different base stations is analyzed, and the user overlap rate between the any two user groups is obtained in combination with the user trajectory weight; Analyzing the time difference between the arbitrary two user group sets in time sequence, and determining the base station overlap between the arbitrary two base stations based on the user overlap rate and the time difference of all arbitrary two user group sets of the arbitrary two base stations; Based on the possible situation that each micro base station is divided into the cell to which each macro base station belongs, each body vector is randomly generated; the base station overlap between two base stations corresponding to each element in each body vector and the distance between the geographical coordinates of the two base stations corresponding to each element are analyzed to determine the adaptability of each body vector; Based on the adaptability of each individual vector, a genetic algorithm is used to obtain the optimal individual vector and perform dynamic cell networking planning for the distributed base station system.

2. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 1, characterized in that: The process of determining the user overlap rate is as follows: Any user group set of any base station is recorded as Y1, and any user group set of any base station in the distributed base station system except the any base station is recorded as Y2; The sum of the user trajectory weights of all users in the intersection of Y1 and Y2 is calculated, recorded as the first sum, the sum of the user trajectory weights of all users in the union of Y1 and Y2 is calculated, recorded as the second sum, and the user overlap rate is obtained by combining the first sum and the second sum.

3. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 2, characterized in that: The user overlap rate is a ratio of the first sum value to the second sum value.

4. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 1, characterized in that: The process of determining the base station overlap is as follows: For the user overlap rate between all any two user group sets of any two base stations, the user overlap rates corresponding to the same time gap are divided into the same category, all the divided categories are arranged in ascending order of the time gap, and the normalized value of the reciprocal of the sequence number of each category after arrangement is obtained, and the average value of all user overlap rates of each category is calculated, and the base station overlap degree is determined based on the normalized value and the average value.

5. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 4, characterized in that: The base station overlap is the cumulative sum of the products of the normalized values ​​of all categories of any two base stations and the average value.

6. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 1, characterized in that: The number of elements in the individual vector is the number of micro base stations in the distributed base station system. If the mth element in the individual vector is n, it means that the mth micro base station is classified into the cell to which the macro base station n belongs.

7. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 1, characterized in that: The determination of the adaptability includes: For any individual vector, the base station overlap between the two base stations corresponding to each element is calculated according to its allocation scheme. If two of the base stations are micro base stations and the corresponding element values ​​in any individual vector are the same, the corresponding base station overlap is eliminated; if two of the base stations are a micro base station and a macro base station, respectively, denoted as micro base station A and macro base station B, and the element value corresponding to micro base station A in any individual vector is macro base station B, the corresponding base station overlap is eliminated; if two of the base stations are macro base stations, the corresponding base station overlap is eliminated; The sum of all remaining base station overlaps after removing the base station overlap from any individual vector is calculated, recorded as the neighboring area interference characteristic value of any individual vector, and the adaptability of any individual vector is obtained by combining the distance between the geographic coordinates of the two base stations corresponding to each element in any individual vector.

8. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 7, characterized in that: The mean value of the distances between the geographical coordinates of the two base stations corresponding to all the elements in any individual vector is calculated, and the adaptability of any individual vector is the product of the neighboring cell interference characteristic value and the mean value.

9. A method for dynamic cell networking planning for a distributed base station system as claimed in claim 1, characterized in that: The optimal individual vector is the individual vector with the smallest adaptability.

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