CN110536338B - Antenna parameter adjustment method and device - Google Patents
Antenna parameter adjustment method and device Download PDFInfo
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Abstract
The embodiment of the invention provides an antenna parameter adjustment method and device. The method comprises the following steps: acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA; establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data; determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm; and adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors. The invention effectively reduces the complexity of data processing, shortens the working period and improves the processing efficiency.
Description
Technical Field
The embodiment of the invention relates to the technical field of mobile communication, in particular to an antenna parameter adjusting method and device.
Background
In a mobile communication network, azimuth angle, downtilt angle, altitude, position and the like of a cell antenna are important engineering parameters, and the rationality of setting determines the coverage effect of the whole network and the perception of users. When the conditions of engineering construction, wireless parameters, user distribution and the like of the mobile communication network change, operators need to adjust antenna parameters in time, and under the condition of not influencing the network coverage integrity, the optimization of user perception is ensured.
The current methods for adjusting the antennas of the cells generally include the following three methods:
firstly, road test adjustment, namely road test, carrying out network pulling test through road test software, and carrying out optimization adjustment on parameters such as azimuth angle, downward inclination angle and the like of an antenna of a cell by network optimization personnel when unreasonable coverage such as weak coverage, over coverage or overlapping coverage is found;
secondly, aiming at the adjustment of the complaints of the user, reversely checking a main service cell when the user complains according to a complaint work order, then associating historical data of the main service cell, and analyzing the coverage condition of the cell; according to the analysis result, the network optimizing personnel optimally adjusts the azimuth angle, the downward inclination angle and other parameters of the cell antenna;
thirdly, based on adjustment Of measurement reports (Measurement Report, MR), according to the measurement reports, smoothing treatment is carried out on reported measurement data Time Advance (TA) and Angle Of Arrival (AOA), iteration is carried out by using a clustering algorithm, particles Of TA and AOA in measurement data Of each cell are calculated, and then azimuth angles and downtilt angles required to be adjusted Of the cells are calculated through the TA and the AOA Of the particles.
However, the above adjustment methods have certain drawbacks, such as:
in the way of adjusting and optimizing the antenna through road test and aiming at user complaints, the angle of antenna adjustment is determined according to the experience of network optimizing personnel, and after adjustment, the antenna is required to be verified and adjusted in the reverse direction until the requirement is met. The process consumes a great deal of manpower and material resources, and has long working period and low efficiency.
In the manner of complaints for users, since the optimal departure point is the complaints of users, the manner is too passive and affects the user experience.
In the adjustment mode based on the measurement report, the defects of great manpower and material consumption, long working period and low efficiency of the two methods are overcome to a certain extent. However, due to the increasing complexity of communication networks and the increasing environmental diversity, the existing technology cannot meet the actual field application; the current technology is mainly aimed at a Time Division synchronous code Division multiple access (TD-Synchronous Code Division Multiple Access) system, but with the exponential increase of the number of 4G users, the number of measurement reports increases dramatically, and the traditional clustering method cannot meet the requirement of big data analysis.
Disclosure of Invention
The embodiment of the invention provides an antenna parameter adjusting method and device, which are used for solving the defect problem in an optimized antenna adjusting mode in the prior art.
In one aspect, an embodiment of the present invention provides a method for adjusting an antenna parameter, where the method includes:
acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
Establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
and adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
In another aspect, an embodiment of the present invention provides an antenna parameter adjustment apparatus, including:
an acquisition module for acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
the establishing module is used for establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
the determining module is used for determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
and the adjusting module is used for adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
In another aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, a bus, and a computer program stored in the memory and capable of running on the processor, where the processor implements the steps in the above-mentioned antenna parameter adjustment method when executing the program.
In yet another aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described antenna parameter adjustment method.
According to the antenna parameter adjustment method and device provided by the embodiment of the invention, the TA and AOA distribution matrix in the MR data is established by acquiring the MR data of the measurement report aiming at the target cell, so that the standardization and high-efficiency storage of the whole network data are realized; the center of gravity position of the branch office matrix is determined by improving a genetic algorithm, the requirement of large data analysis of the existing network is met, and the antenna parameters of a target cell are adjusted according to the MR data of factors of the center of gravity position, so that the complexity of data processing is effectively reduced, the working period is shortened, and the processing efficiency is improved; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating a method for adjusting antenna parameters according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for adjusting antenna parameters according to another embodiment of the present invention;
fig. 3 is a flowchart illustrating a method for adjusting antenna parameters according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of an antenna parameter adjusting device provided by the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 shows a flow chart of an antenna parameter adjustment method according to an embodiment of the present invention.
As shown in fig. 1, the method for adjusting antenna parameters provided by the embodiment of the present invention specifically includes the following steps:
The MR is a main means for obtaining wireless information of a terminal at a network side, and MR data mainly comprises two parts: uplink signal information and downlink signal information. The downlink signal information is measured, collected and reported to the network by the terminal; the uplink signal information is measured and collected by a base transceiver station at the network side. The base transceiver station gathers uplink and downlink measurement information and reports the uplink and downlink measurement information to the base station controller through MR, so that MR data can be acquired through a base station to which a target cell belongs, and MR data can be acquired through a north interface (the north interface is an interface provided for an operator by a device manufacturer) of the base station.
Further, the base station is a macro station, MR data in a preset time period of the base station can be obtained, and the azimuth angle and the downtilt angle of the cell to be adjusted are calculated through TA and AOA in the MR data.
The AOA is a positioning algorithm based on the signal arrival angle, and the algorithm has low communication overhead and higher positioning precision. The TA is measured by the base station and then informs the mobile station to transmit data in advance of the TA time, so as to deduct the transmission delay between the base station and the mobile station.
102, establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data.
The method can realize standardized and efficient storage of the mass data of the whole network by establishing a distribution matrix. Specifically, as an example, the form of the distribution matrix may be as shown in the following table 1:
table 1:
the distribution matrix in table 1 is a matrix of 720×45, and the columns are the distributions of AOA from 0 to 719 in the measurement report, and the distribution matrix increases sequentially from top to bottom; the distribution of TA from 0 to 44 in the behavioural measurement report increases in sequence from left to right in the matrix; the data stored in the matrix is an integer value, representing the number of sample points in the target cell measurement report of aoa=5 and ta=4 in the statistical time, for example, for the number of times the AOA/TA occurs.
As can be seen from table 1 above, the data processing complexity can be effectively reduced and the processing efficiency can be improved by adopting the form of the distribution matrix.
And step 103, determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm.
Specifically, the genetic algorithm is a randomized search method evolved by referring to the evolution law of the biological kingdom, and the improved genetic algorithm improves the convergence accuracy of the genetic algorithm and accelerates the convergence speed by adaptively adjusting the genetic parameters. In the embodiment of the invention, the gravity center position of the distribution matrix, namely the center of mass, and factors of the gravity center position are determined by improving a genetic algorithm, and the distance difference between the gravity center and other factors in the distribution matrix is the smallest, namely the factor with the highest adaptability in the distribution matrix.
And step 103, adjusting antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
After the center of gravity position is determined, the optimal antenna parameters of the target cell can be determined by performing preset data conversion on the MR data, and the antenna parameters of the antenna of the target cell are adjusted according to the optimal antenna parameters so as to optimize the network coverage condition.
In the embodiment of the invention, the distribution matrix of TA and AOA in the MR data is established by acquiring the MR data of the measurement report aiming at the target cell, so that the standardization and the high-efficiency storage of the whole network data are realized; the center of gravity position of the branch office matrix is determined by improving a genetic algorithm, the requirement of large data analysis of the existing network is met, and the antenna parameters of a target cell are adjusted according to the MR data of factors of the center of gravity position, so that the complexity of data processing is effectively reduced, the working period is shortened, and the processing efficiency is improved; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved. The embodiment of the invention solves the problems of long working period, low efficiency, poor user perception, incapability of meeting big data analysis and the like in an optimization adjustment mode aiming at an antenna in the prior art.
Based on the above embodiments, referring to fig. 2, a further embodiment of the present invention provides an antenna parameter adjustment method, which specifically includes the following steps:
The MR is a main means for obtaining wireless information of a terminal at a network side, and MR data mainly comprises two parts: uplink signal information and downlink signal information. The downlink signal information is measured, collected and reported to the network by the terminal; the uplink signal information is measured and collected by a base transceiver station at the network side. The base transceiver station gathers uplink and downlink measurement information and reports the uplink and downlink measurement information to the base station controller through MR, so that MR data can be acquired through a base station to which a target cell belongs, and MR data can be acquired through a north interface (the north interface is an interface provided for an operator by a device manufacturer) of the base station.
Further, the base station is a macro station, MR data in a preset time period of the base station can be obtained, and the azimuth angle and the downtilt angle of the cell to be adjusted are calculated through TA and AOA in the MR data.
The AOA is a positioning algorithm based on the signal arrival angle, and the algorithm has low communication overhead and higher positioning precision. The TA is measured by the base station and then informs the mobile station to transmit data in advance of the TA time, so as to deduct the transmission delay between the base station and the mobile station.
by establishing the distribution matrix, standardization and efficient storage of the mass data of the whole network can be realized, the complexity of data processing can be effectively reduced, and the processing efficiency is improved.
And step 203, screening the initial population in the distribution matrix according to a first preset condition.
The first preset condition is used for screening excellent individuals in the distribution matrix, wherein the excellent individuals are high-quality genes in a genetic algorithm. The concentration condition of AOA/TA is obtained after screening, and the high-quality genes exist in a population with a large number of samples, so that the first preset condition is as follows: and selecting a preset number of individuals with a large number of samples in the distribution matrix as an initial group.
The fitness is an index for measuring the quality of individuals in the population. The adaptability in the genetic algorithm is the value of the criterion of the feature combination, and the selection of the adaptability is the key of the genetic algorithm and is used for evaluating the quality of individuals and taking the quality as the basis of the subsequent genetic operation.
Specifically, step 204 includes:
determining fitness of individuals in the initial population according to the following formula:
wherein f (x, y) is an fitness formula of an individual D (x, y) in the initial population, x is an AOA value of the individual D, y is a TA value of the individual D;
m is the maximum value of x, and N is the maximum value of y;
g (t) is a preset conversion formula between the TA value and the equivalent distance.
If the distribution matrix is a matrix D, D (x, y) represents one factor in the matrix D, and is the number of sample points with aoa=x and ta=y, f (x, y) is the euclidean distance between the individual D (x, y) and other individuals in the distribution matrix. To obtain the concentration of AOA/TA, the minimum distance difference between a certain group of AOA/TA and all the other AOA/TA combinations is calculated.
Taking the distribution matrix in Table 1 as an example, 1.ltoreq.x.ltoreq. 719,1.ltoreq.y.ltoreq.44, that is, M is the maximum value of x and N is the maximum value of y.
g (t) [ g (t), i.e., g (x) or g (y) ] is a preset conversion formula between the TA value and the equivalent distance, and the unit of the equivalent distance is Ts×C. Ts represents the most basic time unit of LTE, C represents the speed of light,
Ts＝1/(15000x 2048)seconds；
wherein g (t) is of the form:
Specifically, the preset filtering operation includes a selection operation, a crossover operation and a mutation operation which are sequentially performed.
A selection operation, taking N individuals as an example, N is a positive integer:
1) Eliminating abnormal individuals (determined by the value range of the AOA/TA);
for example, when an fitness individual with a larger difference from the fitness of other individuals appears in the original fitness table, the individual is determined to be an abnormal individual, the abnormal individual is removed, and after the abnormal individual is removed, the positions of the abnormal individual in the distribution matrix are newly added from the initial population.
2) And sequentially calculating the fitness value of newly added individuals in the population (the initial population needs to be calculated in full quantity), and only keeping the first (N-1) non-repeated individuals with the highest fitness.
3) And adding an individual with the highest fitness in the population.
Still taking Table 1 as an example, AOA is a distribution from 0 to 719, thus requiring a 10-bit unsigned binary integer representation; TA is a distribution from 0 to 44, thus requiring a 6-bit unsigned binary integer representation. The individual basis is therefore a 16-bit unsigned binary integer.
For example, the selection operation is performed on the individual having the largest sample value of the first 10 samples, and the selection result is shown in table 2 below:
Table 2:
wherein, the number 10 individual with obvious abnormal adaptability is removed through the operation 1), so the selection times are 0 times;
the number 8 individual with the highest fitness is selected 2 times since it is added once in operation 3).
And II, performing crossover operation:
individuals in the current population are paired randomly, the positions of the crossing points are set randomly, and part of genes of the paired population are exchanged.
In the random pairing process, the positions of the crossing points are randomly set, and partial genes of the pairing group are exchanged by taking the crossing points as demarcation points.
Specifically, referring to table 3, table 3 is the result of the crossover operation performed on table 2:
table 3:
taking the intersection of the individual 1 and the individual 3 as an example, the corresponding intersection position is 5, and the intersection transposition is performed on the fifth bit from right to left, so that the result after the intersection operation is as follows:
individual No. 1: 0000000001000000,3 individuals: 0000000000000000.
thirdly, mutation operation:
1) Randomly determining whether the individuals after crossing have undergone mutation.
I.e., randomly determining a list of crossover results in table 3, whether the gene is mutated relative to the original individual, i.e., randomly mutating the crossover results, or not.
For example, individuals No. 1, no. 3 and No. 8 in table 3 were mutated.
2) The location of genetic variation in individuals who have undergone variation is randomly determined.
3) After the mutation position of the gene is determined, the gene value of the mutation position is inverted, and thus the mutation operation is completed.
The results after inversion are shown in table 4 below:
table 4:
in table 4, individuals No. 1, no. 3, and No. 8 were mutated; and the position of each variation is randomly determined.
Further, the filtering operation includes an end condition, specifically:
according to the fitness, carrying out preset screening operation on the initial population in an iterative mode;
and when the operation result of the preset screening operation meets a preset ending condition, confirming that the group corresponding to the operation result is a target group.
That is, the above-mentioned preset screening operation is an iterative process, and when the preset end condition is satisfied, the obtained population is the target population.
The end condition may include:
1, after a continuous preset number of iterations, the result of the preset screening operation is the same. And/or
And 2, the iteration times exceed a preset iteration threshold value.
For example, in the condition 1, three iterations are performed continuously, and the results of the preset screening operation are the same, so that the preset ending condition is considered to be satisfied; in condition 2, if the number of iterations exceeds 3, the preset end condition is considered to be satisfied.
And 206, determining the barycenter position of the distribution matrix according to the target group, wherein the barycenter position of the distribution matrix is the individual with the highest fitness in the target group.
Specifically, according to the fitness of a target group, determining the gravity center position of a distribution matrix, wherein the gravity center position of the distribution matrix is the individual with the highest fitness in the target group; the center of gravity is the center of mass, and the center of gravity is the center of gravity, and the distance difference between other factors in the distance distribution matrix is the smallest, namely the factor with the highest adaptability in the distribution matrix.
After the center of gravity position is determined, the optimal antenna parameters of the target cell can be determined by performing preset data conversion on the MR data, and the antenna parameters of the antenna of the target cell are adjusted according to the optimal antenna parameters so as to optimize the network coverage condition.
In the embodiment of the invention, the distribution matrix of TA and AOA in the MR data is established by acquiring the MR data of the measurement report aiming at the target cell, so that the standardization and the high-efficiency storage of the whole network data are realized; through improving a genetic algorithm, determining the barycenter position of the branch office matrix through preset screening operation, meeting the requirement of large data analysis of the existing network, and adjusting the antenna parameters of a target cell according to MR data of factors of the barycenter position, thereby effectively reducing the complexity of data processing, shortening the working period and improving the processing efficiency; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved.
Based on the above embodiments, referring to fig. 3, a further embodiment of the present invention provides an antenna parameter adjustment method, which specifically includes the following steps:
The MR is a main means for obtaining wireless information of a terminal at a network side, and MR data mainly comprises two parts: uplink signal information and downlink signal information. The downlink signal information is measured, collected and reported to the network by the terminal; the uplink signal information is measured and collected by a base transceiver station at the network side. The base transceiver station gathers uplink and downlink measurement information and reports the uplink and downlink measurement information to the base station controller through MR, so that MR data can be acquired through a base station to which a target cell belongs, and MR data can be acquired through a north interface (the north interface is an interface provided for an operator by a device manufacturer) of the base station.
Further, the base station is a macro station, MR data in a preset time period of the base station can be obtained, and the azimuth angle and the downtilt angle of the cell to be adjusted are calculated through TA and AOA in the MR data.
The AOA is a positioning algorithm based on the signal arrival angle, and the algorithm has low communication overhead and higher positioning precision. The TA is measured by the base station and then informs the mobile station to transmit data in advance of the TA time, so as to deduct the transmission delay between the base station and the mobile station.
By establishing the distribution matrix, standardization and efficient storage of the mass data of the whole network can be realized, the complexity of data processing can be effectively reduced, and the processing efficiency is improved.
Specifically, the genetic algorithm is a randomized search method evolved by referring to the evolution law of the biological kingdom, and the improved genetic algorithm improves the convergence accuracy of the genetic algorithm and accelerates the convergence speed by adaptively adjusting the genetic parameters. In the embodiment of the invention, the gravity center position of the distribution matrix and factors of the gravity center position are determined by improving a genetic algorithm, and the distance difference between the gravity center position and other factors in the distribution matrix is the smallest, namely the factor with the highest adaptability in the distribution matrix.
In this step, MR data, AOA and TA, which are factors of the center of gravity position, are converted into standard antenna parameters, i.e., optimal antenna parameters.
The antenna parameters include: azimuth and/or downtilt.
1) The azimuth angle is determined as follows:
η 1 is the azimuth angle in the antenna parameters.
2) The height and downtilt angle are determined as follows:
2.1, first convert TA to a distance formula:
G(TA)＝g(TA)*C*Ts；
wherein G (TA) is the equivalent distance, i.e., height.
2.2, determining the declination angle according to the following formula:
wherein H is 1 Is the height among the antenna parameters.
And step 305, adjusting the antenna of the target cell according to the standard antenna parameters.
After determining the standard antenna parameters, the antenna parameters of the antenna of the target cell are adjusted according to the standard antenna parameters, and the network coverage condition is optimized.
Specifically, when the antenna parameter includes an azimuth angle, step 305 includes:
if the azimuth deviation between the current azimuth of the target cell and the azimuth of the standard antenna parameter exceeds a preset azimuth deviation threshold,
and adjusting the azimuth angle of the antenna of the target cell according to a preset adjustment rule.
Wherein azimuth deviation = AOA converted angle of center of gravity position-azimuth of antenna to be adjusted.
When the azimuth deviation exceeds a preset azimuth deviation threshold, adjusting the azimuth of the antenna of the target cell according to a preset adjustment rule, namely adjusting the azimuth in the opposite direction according to the azimuth deviation so as to eliminate the azimuth deviation; and typically a positive azimuthal deviation represents a clockwise adjustment and a negative represents a counterclockwise adjustment.
Further, as a specific example, after the step of adjusting the azimuth angle of the antenna of the target cell according to a preset adjustment rule, the method further includes: correcting the azimuth angle of the target cell;
i.e. secondarily modifying the antenna parameters of the cell and following the following principle:
for the target modification cell, the target cell with the azimuth deviation meeting a preset threshold is the target modification cell:
1) If the Euclidean distance between the gravity center position of the target modification cell and the gravity center position of the different-station cell is smaller than a certain distance threshold value, only the cell with a relatively large number of gravity center position samples is adjusted.
2) If one cell needs to be adjusted under the base station where the target modification cell is located, then:
and a, if the included angles between the antennas at the same station after adjustment are all between (90 degrees and 150 degrees), the adjustment angle of the cell is the azimuth deviation.
The adjusted antenna in this example is referred to as: and adjusting the antenna of the azimuth angle according to the preset adjustment rule.
And b, if the included angle between the antennas at the same station after adjustment is outside (90 degrees and 150 degrees), calculating that the included angles between the antennas to be adjusted and the antennas at the same station all meet the azimuth angle adjustment range S between (90 degrees and 150 degrees), and taking the value closest to the azimuth angle deviation in the azimuth angle adjustment range S as the final azimuth angle adjustment value of the antenna.
3) If two cells need to be adjusted under the base station where the cell to be adjusted is located, then:
and a, if the included angles between the antennas at the same station after adjustment are all between (90 degrees and 150 degrees), the adjustment angle of the cell is the azimuth deviation.
b, if the included angle between the antennas at the same station after adjustment is outside (90 degrees and 150 degrees), gradually reducing the adjustment angles of the two antennas to be adjusted in a constant amplitude mode, namely, the two antennas to be adjusted have the same degree of each adjustment azimuth angle until the included angle between the antennas to be adjusted and the antennas at the same station is all satisfied (90 degrees and 150 degrees).
4) If more than two cells need to be adjusted under the base station where the cell to be adjusted is located, then:
and a, if the included angles between the antennas at the same station after adjustment are all between (90 degrees and 150 degrees), the adjustment angle of the cell is the azimuth deviation.
b, if the included angle between the antennas at the same station after adjustment is outside (90 degrees, 150 degrees), selecting two antennas with the largest homodromous adjustment angles, and performing the following adjustment:
i. if the included angle between the two antennas is between (90 degrees and 150 degrees) after adjustment, the adjustment angles of the two antennas are azimuth deviation; the remaining antennas are readjusted with reference to principles 2), 3), 4).
if the included angle between the two antennas after adjustment is outside (90 degrees, 150 degrees), referring to the principle 2), and modifying the adjustment angles of the two antennas; the remaining antennas are readjusted with reference to principles 2), 3), 4).
Specifically, when the antenna parameters include downtilt angle and altitude, after step 305, the method includes:
judging that an obstacle exists between the antenna of the target modification cell and the gravity center position according to the height of the standard antenna parameter, the antenna height of the target modification cell and a preset three-dimensional map of the target modification cell;
determining an increased height of the antenna, the increased height being a desired increased height of the antenna to eliminate a barrier effect of the barrier;
and determining the downtilt angle of the antenna according to the increased height.
If it is determined that an obstacle exists between the current antenna height and the height of the center of gravity position of the target modification cell according to the preset three-dimensional map, the antenna height needs to be modified secondarily, and the height of the antenna to be adjusted is increased so as to eliminate the influence of the obstacle.
If the antenna increasing height is smaller than the preset threshold value, the antenna increasing height is increased.
If the antenna increasing height is larger than or equal to the preset threshold value, calculating the antenna declination angle by using the height of the building and the distance between the obstacle and the center of gravity according to a preset algorithm.
Optionally, if there is no obstacle between the antenna height of the target modification cell and the antenna height of the center of gravity position
Downtilt angle = downtilt angle of center of gravity position-downtilt angle of antenna to be adjusted;
and the downward inclination angle is positive, which means that the adjusting direction is downward, namely, the antenna is pressed down.
In the embodiment of the invention, the distribution matrix of TA and AOA in the MR data is established by acquiring the MR data of the measurement report aiming at the target cell, so that the standardization and the high-efficiency storage of the whole network data are realized; the center of gravity position of the branch office matrix is determined by improving a genetic algorithm, the requirement of large data analysis of the existing network is met, and according to MR data of factors of the center of gravity position, the antenna parameters of a target cell are adjusted by combining a preset three-dimensional map, so that the data processing complexity is effectively reduced, the working period is shortened, and the processing efficiency is improved; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved.
Having described the method for adjusting the antenna parameters provided by the embodiment of the present invention, the device for adjusting the antenna parameters provided by the embodiment of the present invention will be described with reference to the accompanying drawings.
Referring to fig. 4, an embodiment of the present invention provides an antenna parameter adjustment device, including:
an acquisition module 401 for acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
a building module 402, configured to build a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
a determining module 403, configured to determine a barycenter position of the distribution matrix according to a preset modified genetic algorithm;
an adjusting module 404, configured to adjust an antenna parameter of an antenna of the target cell according to MR data of the factor of the center of gravity position.
Optionally, in an embodiment of the present invention, the determining module 403 includes:
the first screening submodule is used for screening the initial population in the distribution matrix according to a first preset condition;
the first determining submodule is used for determining the fitness of the individuals in the initial group according to a first preset formula;
The second screening submodule is used for carrying out preset screening operation on the initial population according to the adaptability to obtain a target population;
and the second determination submodule is used for determining the barycenter position of the distribution matrix according to the target group, wherein the barycenter position of the distribution matrix is the individual with the highest adaptability in the target group.
Optionally, in an embodiment of the present invention, the first determining submodule is configured to:
determining fitness of individuals in the initial population according to the following formula:
wherein f (x, y) is an fitness formula of an individual D (x, y) in the initial population, x is an AOA value of the individual D, y is a TA value of the individual D;
m is the maximum value of x, and N is the maximum value of y;
g (t) is a preset conversion formula between the TA value and the equivalent distance.
Optionally, in an embodiment of the present invention, the second screening submodule is configured to:
according to the fitness, carrying out preset screening operation on the initial population in an iterative mode;
and when the operation result of the preset screening operation meets a preset ending condition, confirming that the group corresponding to the operation result is a target group.
Optionally, in an embodiment of the present invention, the adjusting module 404 includes:
A third determining submodule, configured to determine a standard antenna parameter according to MR data of the factor of the barycenter position, where the standard antenna parameter is an antenna parameter of the factor of the barycenter position; the antenna parameters include: azimuth, altitude, and/or downtilt;
and the adjusting sub-module is used for adjusting the antenna of the target cell according to the standard antenna parameters.
Optionally, in an embodiment of the present invention, when the antenna parameter includes an azimuth angle, the adjusting submodule is configured to:
if the azimuth deviation between the current azimuth of the target cell and the azimuth of the standard antenna parameter exceeds a preset azimuth deviation threshold,
and adjusting the azimuth angle of the antenna of the target cell according to a preset adjustment rule.
Optionally, in an embodiment of the present invention, when the antenna parameter includes a downtilt angle and a height, the adjusting submodule is configured to:
judging that an obstacle exists between the antenna of the target cell and the gravity center position according to the height of the standard antenna parameter, the antenna height of the target cell and a preset three-dimensional map of the target cell;
determining an increased height of the antenna, the increased height being a desired increased height of the antenna to eliminate a barrier effect of the barrier;
And determining the downtilt angle of the antenna according to the increased height.
In the above embodiment of the present invention, the acquisition module 401 acquires the MR data of the measurement report for the target cell, and the establishing module 402 establishes the distribution matrix of TA and AOA in the MR data, so as to realize standardization and efficient storage of the whole network data; the determining module 403 determines the barycenter position of the branch office matrix by improving the genetic algorithm, so as to meet the requirement of the analysis of the current network big data, and the adjusting module 404 adjusts the antenna parameters of the target cell according to the MR data of the factors of the barycenter position, thereby effectively reducing the complexity of data processing, shortening the working period and improving the processing efficiency; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved. Fig. 5 is a schematic structural diagram of an electronic device according to another embodiment of the present invention.
Referring to fig. 5, an electronic device according to an embodiment of the present invention includes a memory (memory) 51, a processor (processor) 52, a bus 53, and a computer program stored on the memory 51 and executable on the processor. Wherein the memory 51 and the processor 52 complete communication with each other through the bus 53.
The processor 52 is configured to invoke program instructions in the memory 51 to implement the method of fig. 1 when executing the program.
In another embodiment, the processor, when executing the program, implements the following method:
acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
and adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
The electronic device provided in the embodiment of the present invention may be used to execute the program corresponding to the method in the embodiment of the method, and this implementation is not repeated.
According to the electronic equipment provided by the embodiment of the invention, the distribution matrix of TA and AOA in the MR data is established by acquiring the MR data of the measurement report aiming at the target cell, so that the standardization and the high-efficiency storage of the whole network data are realized; the center of gravity position of the branch office matrix is determined by improving a genetic algorithm, the requirement of large data analysis of the existing network is met, and the antenna parameters of a target cell are adjusted according to the MR data of factors of the center of gravity position, so that the complexity of data processing is effectively reduced, the working period is shortened, and the processing efficiency is improved; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved.
A further embodiment of the invention provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps as in fig. 1.
In another embodiment, the program when executed by a processor implements the method of:
acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
and adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
The non-transitory computer readable storage medium provided in the embodiment of the present invention realizes the method of the above method embodiment when the program is executed by the processor, and this implementation is not repeated.
The non-transitory computer readable storage medium provided by the embodiment of the invention realizes standardized and efficient storage of whole-network data by acquiring measurement report MR data aiming at a target cell and establishing a distribution matrix of TA and AOA in the MR data; the center of gravity position of the branch office matrix is determined by improving a genetic algorithm, the requirement of large data analysis of the existing network is met, and the antenna parameters of a target cell are adjusted according to the MR data of factors of the center of gravity position, so that the complexity of data processing is effectively reduced, the working period is shortened, and the processing efficiency is improved; in the embodiment of the invention, the antenna parameter adjustment optimization is actively initiated, and the user experience is improved.
Yet another embodiment of the present invention discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example comprising:
acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
and adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An antenna parameter adjustment method, comprising:
acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
according to the MR data of the gravity center position factors, adjusting the antenna parameters of the antenna of the target cell;
wherein, the determining the barycenter position of the distribution matrix according to a preset improved genetic algorithm includes:
screening an initial population in the distribution matrix according to a first preset condition;
determining the fitness of the individuals in the initial population according to a first preset formula;
according to the fitness, carrying out preset screening operation on the initial population to obtain a target population;
determining the gravity center position of the distribution matrix according to the target group, wherein the gravity center position of the distribution matrix is the individual with the highest adaptability in the target group;
The first preset formula is:
wherein f (x, y) is an fitness formula of an individual D (x, y) in the initial population, x is an AOA value of the individual D, y is a TA value of the individual D;
m is the maximum value of x, and N is the maximum value of y;
g (t) is a preset conversion formula between the TA value and the equivalent distance.
2. The method of claim 1, wherein the step of performing a preset screening operation on the initial population according to the fitness to obtain a target population comprises:
according to the fitness, carrying out preset screening operation on the initial population in an iterative mode;
and when the operation result of the preset screening operation meets a preset ending condition, confirming that the group corresponding to the operation result is a target group.
3. The method of claim 1, wherein the step of adjusting antenna parameters of the antenna of the target cell based on MR data of the factors of the center of gravity position comprises:
determining standard antenna parameters according to the MR data of the gravity center position factors, wherein the standard antenna parameters are the antenna parameters of the gravity center position factors; the antenna parameters include: azimuth, altitude, and/or downtilt;
And adjusting the antenna of the target cell according to the standard antenna parameters.
4. A method according to claim 3, wherein when the antenna parameters include azimuth angles, the step of adjusting the antenna of the target cell according to the standard antenna parameters comprises:
if the azimuth deviation between the current azimuth of the target cell and the azimuth of the standard antenna parameter exceeds a preset azimuth deviation threshold,
and adjusting the azimuth angle of the antenna of the target cell according to a preset adjustment rule.
5. A method according to claim 3, wherein, when the antenna parameters include downtilt and altitude, after the step of adjusting the antenna of the target cell according to the standard antenna parameters, the method further comprises:
judging that an obstacle exists between the antenna of the target cell and the gravity center position according to the height of the standard antenna parameter, the antenna height of the target cell and a preset three-dimensional map of the target cell;
determining an increased height of the antenna, the increased height being a desired increased height of the antenna to eliminate a barrier effect of the barrier;
And determining the downtilt angle of the antenna according to the increased height.
6. An antenna parameter adjustment device, comprising:
an acquisition module for acquiring measurement report MR data for a target cell; the MR data at least comprises a measurement data time advance TA and an arrival angle ranging AOA;
the establishing module is used for establishing a distribution matrix of the TA and the AOA; wherein the data corresponding to the factor D (m, n) in the distribution matrix is the number of occurrences of an individual of aoa=m and ta=n in the MR data;
the determining module is used for determining the gravity center position of the distribution matrix according to a preset improved genetic algorithm;
the adjusting module is used for adjusting the antenna parameters of the antenna of the target cell according to the MR data of the gravity center position factors;
the determining module is further configured to:
screening an initial population in the distribution matrix according to a first preset condition;
determining the fitness of the individuals in the initial population according to a first preset formula;
according to the fitness, carrying out preset screening operation on the initial population to obtain a target population;
determining the gravity center position of the distribution matrix according to the target group, wherein the gravity center position of the distribution matrix is the individual with the highest adaptability in the target group;
The first preset formula is:
wherein f (x, y) is an fitness formula of an individual D (x, y) in the initial population, x is an AOA value of the individual D, y is a TA value of the individual D;
m is the maximum value of x, and N is the maximum value of y;
g (t) is a preset conversion formula between the TA value and the equivalent distance.
7. An electronic device comprising a memory, a processor, a bus and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the antenna parameter adjustment method according to any one of claims 1-5 when the program is executed.
8. A non-transitory computer readable storage medium having a computer program stored thereon, characterized by: the program, when executed by a processor, implements the steps of the antenna parameter adjustment method according to any one of claims 1-5.
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