Distributed base station system-oriented cell dynamic networking planning method

Abstract

The invention relates to the technical field of cell dynamic networking planning, in particular to a cell dynamic networking planning method for a distributed base station system, and the method comprises the steps: enabling the distributed base station system to comprise macro base stations and micro base stations, and recording the macro base stations and the micro base stations as base stations; forming a user group set of any base station by identity codes of users communicating with any base station within a preset duration, and determining a user track weight of each user at each moment; determining the base station overlapping degree between any two base stations; randomly generating individual vectors on the basis of the possible condition that each micro base station is divided into a cell to which each macro base station belongs; determining the self-adaption degree of each individual vector; and based on the self-adaption degree of each individual vector, obtaining an optimal individual vector by using a genetic algorithm, and performing cell dynamic networking planning on the distributed base station system. According to the invention, the rationality of cell dynamic networking planning is improved, and the communication quality is improved.

Description

Technical Field

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

[0002] The existing network communication system is composed of multiple base stations, and users access the Internet by communicating with base stations. In core areas such as cities, where buildings are densely packed and the volume of business is large, the existing base station system is difficult to provide sufficient communication services. Therefore, micro base stations are added to the existing base station system, resulting in a distributed base station system that combines macro base stations and micro base stations.

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

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

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

[0006] A method for dynamic cell networking planning for a distributed base station system in the present application adopts the following technical solution: An embodiment of the present application provides a method for dynamic cell networking planning for a distributed base station system, the method comprising 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.

[0007] In one embodiment, 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.

[0008] In one embodiment, the user overlap rate is a ratio of the first sum value to the second sum value.

[0009] In one embodiment, 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.

[0010] In one embodiment, 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.

[0011] In one embodiment, 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.

[0012] In one embodiment, 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.

[0013] In one embodiment, the average value of the distances between the geographical coordinates of two base stations corresponding to all 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 average value.

[0014] In one embodiment, the optimal individual vector is an individual vector with the smallest adaptability.

[0015] This application has at least the following beneficial effects: The present application records each macro base station and each micro base station included in the distributed base station system as a base station; obtains the geographical coordinates of each base station; encodes the identity of the user who has been in communication with any base station within a preset time period to form a user group set of the any base station, and obtains a preset number of user group sets of each base station; determines the user trajectory weight of each user at each moment 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 reflects the user's position movement through the frequency of the user switching base stations, and gives a higher weight to users with a smaller activity range to reduce the impact of frequent base station switching caused by rapid user movement on neighboring cell interference judgment; analyzes the degree of user overlap between any two user group sets belonging to different base stations, and obtains the user overlap rate between the any two user group sets in combination with the user trajectory weight; the user overlap rate represents the overlap of users managed by two base stations, and the signal overlap of the two base stations is evaluated by the number of the same users managed by the two base stations to complete the neighboring cell interference estimation. Compared with the existing method of judging neighboring cell interference by geographical location, the present application can avoid mistakenly judging densely built-up areas, uninhabited areas, etc. as signal overlapping areas, thereby reducing the error of neighboring cell interference judgment; analyzing the time difference corresponding to the timing of the arbitrary two user group sets, based on the user overlap rate and the time difference of all arbitrary two user group sets of any two base stations, determine the base station overlap between the arbitrary two base stations; the base station overlap further reflects the user overlap between the two base stations from the time dimension of the user overlap rate, which is helpful to make a more accurate judgment on the neighboring cell interference between the base stations; based on the division of each micro base station into each macro base station The method randomly generates individual vectors according to the possible situations of the cell; analyzes the base station overlap between any two base stations in each individual vector and the distance between the geographical coordinates of any two base stations to determine the adaptability of each individual vector; the adaptability comprehensively reflects the neighboring cell interference and communication delay between base stations; based on the adaptability of each individual vector, the genetic algorithm is used to obtain the optimal individual vector, and the dynamic cell networking planning of the distributed base station system is carried out; 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 neighboring cell interference and communication delay, improving the rationality of the dynamic cell networking planning, and thus improving the communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of the steps of a method for dynamic cell networking planning for a distributed base station system provided in this application; Figure 2 Schematic diagram of the distribution of macro base stations and micro base stations; Figure 3 Determine the flow chart for the degree of adaptability. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a cell dynamic networking planning method for a distributed base station system proposed in accordance with the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0020] The following is a detailed description of a specific scheme of a method for dynamic cell networking planning for a distributed base station system provided by the present application in conjunction with the accompanying drawings.

[0021] An embodiment of the present application provides a method for dynamic cell networking planning for a distributed base station system. Specifically, the following method for dynamic cell networking planning for a distributed base station system is provided. Figure 1 , the method comprises the following steps: Step S001, 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 been communicating 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.

[0022] For a distributed base station system, common factors affecting network quality are communication delays between base stations and interference between neighboring cells. A distributed base station system includes macro base stations and micro base stations. The distribution diagram of macro base stations and micro base stations is shown in the figure below. Figure 2 As shown, Figure 2There is 1 macro base station and 5 micro base stations in the network. There is communication between the micro base stations and the macro base stations. The ellipse surrounding the micro base stations represents the signal coverage of the micro base stations. If there is an overlapping area where the signal coverage of multiple micro base stations is overlapped, when these micro base stations are not managed by the same macro base station, users in this area are prone to neighboring cell interference due to the lack of coordination of the macro base station. The communication delay is because the micro base stations in the same cell need to send the collected information to the macro base station, which will cause communication delay. Generally, the shorter the distance between the micro base station and the macro base station, the smaller the communication delay.

[0023] Neighboring cell interference usually occurs between two different cells. When a user is located at the junction of two cells, the user at the junction will be affected by the signals from two base stations in different cells. These signals will interfere with each other and affect the communication quality. If two different base stations are from the same cell, they are controlled by the same macro base station. The control system of the macro base station will coordinate the transmission of signals for the two cells to reduce the impact of signal interference.

[0024] Therefore, when cells are dynamically divided, it is necessary to improve the communication quality from two aspects: communication delay and neighboring cell interference.

[0025] In the distributed base station system of this embodiment, there are a total of K=100 base stations, of which there are a total of N=9 macro base stations corresponding to 9 cells, and the rest are M=91 micro base stations, and both macro base stations and micro base stations are recorded as base stations. Obtain the geographic 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 limit it here.

[0026] Taking the kth base station in the distributed base station system as an example, the identity codes of users who have communicated with the kth base station within a preset time length are used to form a user group set of the kth base station. The preset time length in this embodiment is 1 minute, and the implementer can set it according to the actual situation. This embodiment does not limit it. The user group set of each minute of the kth base station is obtained within an hour before each moment. Therefore, for the kth base station at each moment, this embodiment obtains T=60 user group sets, corresponding to 60 minutes of the past hour at each moment.

[0027] The same method as that used by the kth base station to obtain the user group set is used to obtain T user group sets of each base station in the distributed base station system at each time, where the number of user group sets can be set by the implementer. The user group set of each base station at each time is recorded as base station monitoring data.

[0028] Step S002: determine the user trajectory weight of each user at each moment based on the number of base stations with which each user communicates within the preset time period before each moment; analyze the degree of user overlap between any two user group sets belonging to different base stations, and obtain the user overlap rate between the any two user group sets in combination with the user trajectory weight.

[0029] In order to improve the communication quality provided by different micro base stations in the distributed base station system, this embodiment divides cells according to the base station monitoring data collected in real time.

[0030] This embodiment divides cells in hours. The specific method is as follows: every hour, the micro base stations need to be re-divided into cells based on user information in the past hour.

[0031] It should be noted that, for the newly deployed micro base stations, since their working time is less than one hour, the cell where the nearest macro base station is located is selected according to the Euclidean distance between them and the geographical coordinates of N macro base stations, and the new micro base stations are added; for the newly deployed macro base stations, since their working time is less than one hour, they will be operated as a separate base station within this hour and will be added to the distributed base station system in the next hour.

[0032] When judging neighboring cell interference, the existing methods usually evaluate the neighboring cell interference situation by the geographical distance between base stations. In the actual micro base station use area, there are usually relatively dense buildings. The base stations with relatively close geographical locations may not have actual users in the intersection area of ​​signal coverage, and thus there may be no neighboring cell interference situation. Therefore, the existing neighboring cell interference estimation method has the problem of inaccurate judgment, which leads to unreasonable cell division and reduces communication quality. In this regard, this embodiment judges the neighboring cell interference situation between base stations based on the historical interaction data between base stations and users.

[0033] First, this embodiment evaluates the neighboring cell interference between base stations by switching between different base stations by users. If a user switches back and forth between a few different base stations in a short period of time, the reason for the user switching base stations may be that the area where the user is located is the signal overlap area between these different base stations. If a user switches between a large number of different base stations in a short period of time, the reason for the user switching base stations may be the base station switching phenomenon caused by the user's own rapid movement in a short period of time.

[0034] Based on the above analysis, this embodiment calculates the user trajectory weight of each user at each time to characterize whether the user is moving quickly at each time. Specifically, at time t, the number of base stations with which the rth user established communication within one minute before time t is obtained through the user group set of K base stations at this time, recorded as S, and the inverse of S is calculated as the user trajectory weight of the rth user at time t. The user trajectory weight of each user at each time is obtained by using the same calculation method as the user trajectory weight of the rth user at time t.

[0035] It should be understood that the greater the user trajectory weight, the fewer base stations the r-th user switches in a short period of time. At this time, the smaller the activity range of the r-th user in a short period of time, the more likely the base station switching phenomenon of the r-th user is caused by the user himself being in the signal overlap area of ​​different base stations, rather than the user's own rapid movement; and thus at time t, the r-th user can better reflect the existence of signal overlap areas between the different base stations he communicates with. Furthermore, in the subsequent neighboring cell interference assessment, the greater the user trajectory weight of the r-th user, the greater the weight given to the r-th user, so as to achieve a more accurate neighboring cell interference assessment effect.

[0036] At the current moment, the distributed base station system Base stations and For example, Base stations and Each base station has T=60 user groups. Select one user group set from the T user groups of the base stations, denoted as Y1, and start from the Select any one user group set from the T user group sets of the base stations, denoted as Y2.

[0037] The intersection and union of the user group set Y1 and the user group set Y2 are calculated respectively, and the sum of the user trajectory weights of all users in the intersection is calculated, which is recorded as the first sum. The sum of the user trajectory weights of all users in the union is calculated, which is recorded as the second sum. The ratio of the first sum to the second sum is used as the user overlap rate between the user group set Y1 and the user group set Y2.

[0038] It should be understood that the first sum value and the second sum value are both equivalent to the number of users counted with the user trajectory weight as the weight. Therefore, the first sum value can be regarded as the number of common users of the two user group sets, and the second sum value can be regarded as the total number of users of the two user group sets. The user trajectory weight assigns weights to users in the statistical process during statistics. The larger the weight, the smaller the moving distance of the user in a short time, and the more important the analysis of these users is when judging whether there is interference from neighboring areas.

[0039] The user overlap rate is the ratio of the first sum value to the second sum value. The larger the value, the higher the overlap rate. Base stations and The more users switch back and forth between base stations, the Base stations and The more likely that there is an overlapping signal coverage area between two base stations, the more likely that there is interference between the two neighboring base stations. Base stations and The base stations should be divided into the same cell in the subsequent cell division to reduce the interference of neighboring cells. Compared with the existing neighboring cell interference determined by geographical distance, the user overlap rate described in this application reflects the overlap degree of the communication areas of two base stations through the historical communication behavior of users, which can avoid treating locations such as densely built areas and uninhabited areas as signal overlap areas, resulting in errors in the judgment of neighboring cell interference.

[0040] Step S003, analyzing the time difference between the arbitrary two user groups 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 groups of any two base stations.

[0041] For Base stations and base stations, using the above calculation method of user overlap rate, a total of The user overlap rate is obtained based on the comparison of user group sets, and different user group sets correspond to different times. Therefore, this embodiment further calculates the base station overlap between two base stations from the time dimension of the user overlap rate, which helps to make the judgment of the neighboring area interference situation between the base stations more accurate.

[0042] First, for the Base stations and The p-th user overlap rate in the base station is calculated, and the time difference between the two user group sets corresponding to the p-th user overlap rate is calculated, that is, the time gap. For example, one of the user group sets corresponding to the p-th user overlap rate is the p-th user group set. The third minute of the hour before the current time of the base station, and another user group set is If the time difference between the two user groups corresponding to the pth user overlap rate is 10 minutes within an hour before the current time of the pth base station, then the time difference between the two user groups is equal to 7; generally, the smaller the time difference between the two user groups is, the shorter the time it takes for the user to switch between the two base stations, and the more likely it is that there will be an overlapping area of ​​signal coverage between the two base stations.

[0043] Further, for the Base stations and base stations, where the time difference between two user groups is an integer between [0, 59]. The same time difference is divided into one category. The user overlap 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.

[0044] Finally, the average of the overlap rates of all users in each category is calculated, and the normalized value of the reciprocal of the number of each category is calculated. Specifically, the cumulative sum of the reciprocals of the numbers of T categories is calculated, and the ratio of the reciprocal of the number of each category to the cumulative sum is calculated as the normalized value of the reciprocal of the number of each category, which is recorded as the time weight of each category. Among them, the smaller the time difference of the i-th category, the larger the time weight, which means that the user is in the i-th category. Base stations and The shorter the time it takes to switch between base stations, the more the user overlap rate can explain the Base stations and There are overlapping areas of signal coverage between base stations.

[0045] Therefore, taking the time weight as the weight, the weighted sum of the mean of the overlap rate of all users in each category of T categories is used as the first Base stations and The base station overlap between base stations reflects the Base stations and The greater the base station overlap, the greater the base station overlap. Base stations and The more likely that there will be overlapping areas of signal coverage for each base station, the Base stations and The more base stations there are in neighboring areas, the more serious the interference is.

[0046] Adopt and Base stations and The base station overlap degree between any two base stations in the distributed base station system can be obtained by using the same calculation method for the base station overlap degree between the base stations.

[0047] Step S004, 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.

[0048] For cell network planning, this embodiment first generates multiple random cell division schemes, and selects the optimal cell division scheme in combination with base station overlap and communication delay. The specific process is as follows: First, randomly generate An individual vector of length M, where the elements in the individual vector are integers from 1 to N. For example, the mth element in the vector is n, which means that the mth micro base station is classified into the cell to which the macro base station n belongs. That is, the value range of the elements in the individual vector, the integers from 1 to N correspond to the N macro base stations, and the position of each element in the individual vector corresponds to the M micro base stations.

[0049] In this embodiment The implementer can set it according to the actual situation, and this embodiment does not limit it.

[0050] Secondly, The adaptability of each individual vector is calculated separately, and the adaptability of the cth individual vector is calculated as follows: For K base stations in the distributed base station system, the base station overlap between any two base stations is calculated. Further, all the calculated base station overlaps are screened. The screening method is: each base station overlap corresponds to two base stations, and the two base stations are analyzed: In the first case, if the two base stations are micro base stations, the element values ​​corresponding to the two micro base stations are found in the cth individual vector. If the element values ​​are the same, it means that according to the cell division scheme of the cth individual vector, the two base stations are divided into the same cell. At this time, the base station overlap of the two base stations is eliminated; otherwise, the two base stations are divided into different cells, and the base station overlap of the two base stations is retained; In the second case, if one of the 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 the element corresponding to micro base station A is found in the cth 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 cth individual vector, micro base station A and macro base station B are divided into the same cell. At this time, the base station overlap between micro base station A and macro base station B is eliminated; otherwise, micro base station A and macro base station B are divided into different cells, and the base station overlap between micro base station A and macro base station B is retained; In the third case, if the two base stations are both macro base stations, the two base stations cannot be divided into the same cell, which has no impact on the cell division, and the base station overlap of the two base stations is eliminated.

[0051] In the first and second cases, when two base stations are assigned to the same cell, the two base stations will communicate with the user under the coordination of the cell's internal control system. At this time, the overlapping area covered by the communication signals of the two base stations will not cause neighboring cell interference. Therefore, when dividing the cell through neighboring cell interference screening, there is no need to consider the overlapping signal coverage areas of the two base stations, and then eliminate the base station overlap between the two base stations when calculating the adaptability.

[0052] Using the analysis methods of the above three cases, all the calculated base station overlaps are screened in turn. Finally, the sum of the retained base station overlaps is used as the neighboring cell interference characteristic value of the c-th individual vector. The larger the neighboring cell interference characteristic value is, the more serious the neighboring cell interference phenomenon of the entire distributed base station system is under the cell division scheme of the c-th individual vector, and the cell division scheme of the c-th individual vector should not be selected.

[0053] At the same time, if the value of the mth element in the cth individual vector is n, it means that the mth micro base station and the macro base station n are divided into the same cell. Generally, the farther the distance between the two is, the greater the communication delay. Therefore, the Euclidean distance between the geographical coordinates of the mth micro base station and the macro base station n is calculated as the communication delay corresponding to the mth element value; then, each element of the cth individual vector is traversed, and the communication delays of all elements in the cth individual vector are averaged as the overall communication delay of the cth individual vector. The larger the overall communication delay, the greater the base station communication delay caused by the cell division scheme of the cth individual vector, and the cell division scheme of the cth individual vector should not be selected.

[0054] Finally, the product of the overall communication delay of the cth individual vector and the neighboring cell interference characteristic value of the cth individual vector is calculated and recorded as the adaptability of the cth individual vector. The greater the adaptability, the greater the neighboring cell interference and communication delay caused by the cell division scheme of the cth individual vector, and the cth individual vector should not be retained. The adaptability determination flow chart is shown in Figure 3 shown.

[0055] The adaptability of each randomly acquired individual vector can be obtained by using the same calculation method as that of the adaptability of the cth individual vector.

[0056] Step S005, based on the adaptability of each individual vector, a genetic algorithm is used to obtain the optimal individual vector, and dynamic cell networking planning is performed for the distributed base station system.

[0057] This embodiment The optimization process of individual vectors is: keep the vector with the smallest adaptability individual vectors, as elite individual vectors; in this implementation, the number of elite individuals is Then, Elite individual vectors are crossover and mutation operations are performed. In this embodiment, the crossover rate is set to 0.25, the mutation rate is set to 0.05, and the population size is set to , crossover and mutation operations are the contents of genetic algorithm, which is a well-known technology; the final output is individual vectors; The latest The individual vectors are used as the individual vectors of this cycle, and the adaptability of the latest individual vectors is calculated. The optimization process is repeated until the cycle reaches So far, the last cycle Individual vectors are selected from which the individual vector with the smallest adaptability is used as the final cell division vector. , , The implementer can set the value according to the actual situation, and this embodiment does not limit it.

[0058] Finally, the cell division vector length is M, where if the value of the mth element is n, it means that the mth micro base station is divided into the cell to which the macro base station n belongs, completing the dynamic cell networking planning of the distributed base station system.

[0059] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0061] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in 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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