CN112243242A - Large-scale antenna beam configuration method and device - Google Patents
Large-scale antenna beam configuration method and device Download PDFInfo
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- CN112243242A CN112243242A CN201910645730.XA CN201910645730A CN112243242A CN 112243242 A CN112243242 A CN 112243242A CN 201910645730 A CN201910645730 A CN 201910645730A CN 112243242 A CN112243242 A CN 112243242A
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- H04B7/0413—MIMO systems
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Abstract
The embodiment of the invention provides a method and a device for configuring large-scale antenna beams, wherein the method comprises the following steps: acquiring cell characteristics of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics; inputting the cell characteristics into a beam configuration model, and acquiring a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training; and configuring large-scale antenna beams of the cell to be configured based on the beam template. According to the method and the device provided by the embodiment of the invention, the application of the beam configuration model can accurately and rapidly match the best beam template for the cell to be configured, the defects of poor accuracy and low efficiency of manually selecting the beam template are overcome, the large-scale antenna beam configuration efficiency is improved, the labor cost and the time cost are reduced, and the improvement of the network performance is realized.
Description
Technical Field
The present invention relates to the field of mobile communications technologies, and in particular, to a method and an apparatus for configuring a large-scale antenna beam.
Background
With the rapid development of mobile services and the rapid popularization of intelligent terminals, wireless data services have shown an explosive growth trend, and a fifth Generation mobile communication technology (5th-Generation, 5G) has been developed. The large-scale antenna (Massive MIMO) is used as a key technology of 5G, can generate a high-gain adjustable shaped beam, thereby obviously improving signal coverage, and can greatly reduce interference to the periphery due to the very narrow beam.
In order to match the service channel capacity, the 5G network performs beam forming and beam scanning based on a large-scale antenna to enhance channel coverage, at the moment, the horizontal dimension and the vertical dimension of a channel can both send dynamic narrow beams, and the coverage capacity and the coverage range are considered through the beam scanning when the narrow beams are sent. Fig. 1 is a comparison graph of horizontal dimension beam coverage of 4G and 5G in the prior art, fig. 2 is a comparison graph of vertical dimension beam coverage of 4G and 5G in the prior art, in fig. 1 and 2, a blank sector represents a 4G coverage, and an oblique-dashed filled sector represents a 5G coverage. As shown in fig. 1, in the horizontal dimension, the 5G network uses a plurality of narrow beams to improve the coverage strength and increase the coverage distance. As shown in fig. 2, in the vertical dimension, the 5G network also adopts a plurality of narrow beams to expand the coverage, thereby solving the problem of poor coverage of the 4G high-rise building. Therefore, the planning optimization of the 5G large-scale antenna beam has a great influence on 5G coverage, interference and mobility management, so that the construction, planning optimization and user experience of the whole 5G network are directly influenced.
Currently, the large-scale antenna beam configuration method mainly selects one set from a plurality of sets of beam templates manually for static configuration. However, due to the application of beam scanning, the configuration attributes of large-scale antenna beams are increased, the number of corresponding combinations is also greatly increased, and it is almost impossible to manually select an optimal set of configurations from so many beam templates, which results in the suboptimal network performance. Moreover, the 5G network is an ultra-dense networking, the number of base stations is greatly increased, the number of the 5G base stations in a first-line city can exceed 10 ten thousand, so that the number of the base stations is manually selected, and the efficiency is very low.
Disclosure of Invention
The embodiment of the invention provides a large-scale antenna beam configuration method and device, which are used for solving the problems that the existing manual configuration method is low in efficiency and cannot achieve optimal performance.
In a first aspect, an embodiment of the present invention provides a large-scale antenna beam configuration method, including:
acquiring cell characteristics of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics;
inputting the cell characteristics of the cell to be configured into a beam configuration model, and acquiring a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training;
and configuring the large-scale antenna beams of the cell to be configured based on the beam template.
Preferably, the inputting the cell characteristics of the cell to be configured to a beam configuration model, and obtaining a beam template output by the beam configuration model, before further comprising:
constructing a network simulation model based on an electronic map, a wireless propagation model and the sample base station characteristics of the sample cell;
configuring the sample cell in the network simulation model based on any preset beam template, and acquiring wireless coverage simulation data of the sample cell;
acquiring a beam performance index of the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
selecting the preset beam template corresponding to the maximum value of the beam performance index as the sample beam template of the sample cell;
training the beam configuration model based on the sample cell features and the sample beam template.
Preferably, the obtaining, based on the wireless coverage simulation data, a beam performance index of the sample cell under any preset beam template configuration specifically includes:
acquiring the number of coverage grids and the number of interference grids which are met by the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
and acquiring the beam performance index based on the number of the coverage grids and the number of the interference grids.
Preferably, the training the beam configuration model based on the sample cell characteristics and the sample beam template specifically includes:
training the beam configuration model using a K-nearest neighbor algorithm based on the sample cell features and the sample beam template.
Preferably, the training the beam configuration model based on the sample cell characteristics and the sample beam template further includes:
acquiring coverage data of the sample cell based on the network simulation model;
preprocessing the coverage range data; the preprocessing comprises de-duplication and/or de-outlier;
and acquiring the sample cell characteristics based on the coverage data.
Preferably, the acquiring the cell characteristics of the cell to be configured specifically includes:
acquiring the coverage area of a cell to be configured;
and acquiring the building characteristic, the base station characteristic and the ground feature characteristic of the cell to be configured based on the coverage range of the cell to be configured.
Preferably, the acquiring the coverage area of the cell to be configured specifically includes:
and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
Preferably, the building characteristics include at least one of building height information, building area information, and building location information;
the base station characteristics comprise at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position;
the feature of the feature comprises the area ratio of the covered lower chamber.
Preferably, the building height information is a short building, a medium building or a high building, the building area information is a general building or a large building, the building position information is a distance grade between the building and the base station, and the distance grade is one of a near distance, a medium distance and a far distance.
In a second aspect, an embodiment of the present invention provides a large-scale antenna beam configuration apparatus, including:
a cell feature acquiring unit, configured to acquire a cell feature of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics;
a beam template obtaining unit, configured to input the cell characteristics of the cell to be configured to a beam configuration model, and obtain a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training;
and the beam configuration unit is used for configuring the large-scale antenna beam of the cell to be configured based on the beam template.
Preferably, the method further comprises the following steps:
the network simulation model building unit is used for building a network simulation model based on an electronic map, a wireless propagation model and the sample base station characteristics of the sample cell;
a wireless coverage data obtaining unit, configured to configure the sample cell in the network simulation model based on any preset beam template, and obtain wireless coverage simulation data of the sample cell;
a beam performance index obtaining unit, configured to obtain a beam performance index of the sample cell under any preset beam template configuration based on the wireless coverage simulation data;
a sample beam template selecting unit, configured to select the preset beam template corresponding to the maximum value of the beam performance index as the sample beam template of the sample cell;
a model training unit, configured to train the beam configuration model based on the sample cell characteristics and the sample beam template.
Preferably, the beam performance index obtaining unit is specifically configured to:
acquiring the number of coverage grids and the number of interference grids which are met by the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
and acquiring the beam performance index based on the number of the coverage grids and the number of the interference grids.
Preferably, the model training unit is specifically configured to:
training the beam configuration model using a K-nearest neighbor algorithm based on the sample cell features and the sample beam template.
Preferably, the method further comprises a sample cell feature extraction unit; the sample cell feature extraction unit is configured to:
acquiring coverage data of the sample cell based on the network simulation model;
preprocessing the coverage range data; the preprocessing comprises de-duplication and/or de-outlier;
and acquiring the sample cell characteristics based on the coverage data.
Preferably, the cell characteristic acquiring unit includes:
a coverage obtaining subunit, configured to obtain a coverage of the cell to be configured;
and the characteristic obtaining subunit is configured to obtain the building characteristic, the base station characteristic and the feature characteristic of the cell to be configured based on the coverage area of the cell to be configured.
Preferably, the coverage obtaining subunit is specifically configured to:
and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
Preferably, the building characteristics include at least one of building height information, building area information, and building location information;
the base station characteristics comprise at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position;
the feature of the feature comprises the area ratio of the covered lower chamber.
Preferably, the building height information is a short building, a medium building or a high building, the building area information is a general building or a large building, the building position information is a distance grade between the building and the base station, and the distance grade is one of a near distance, a medium distance and a far distance.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a bus, where the processor and the communication interface, the memory complete communication with each other through the bus, and the processor may call a logic instruction in the memory to perform the steps of the method provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
According to the method and the device for configuring the large-scale antenna beam, the beam template is obtained by inputting the cell characteristics of the cell to be configured into the beam configuration model, and then large-scale antenna beam configuration is realized, wherein the application of the beam configuration model can accurately and rapidly match the optimal beam template for the cell to be configured, the defects of poor accuracy and low efficiency of manually selecting the beam template are overcome, the efficiency of configuring the large-scale antenna beam is improved, the labor cost and the time cost are reduced, and the improvement of the network performance is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a comparison graph of beam coverage in horizontal dimensions of 4G and 5G in the prior art;
FIG. 2 is a comparison graph of the beam coverage in the vertical dimension of 4G and 5G in the prior art;
fig. 3 is a flowchart illustrating a method for configuring a large-scale antenna beam according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a method for configuring a large-scale antenna beam according to another embodiment of the present invention;
fig. 5 is a schematic flowchart of a method for obtaining a sample beam template according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of a method for training a beam configuration model according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a large-scale antenna beam configuration apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The existing large-scale antenna beam configuration method is to manually select one set from a plurality of sets of beam templates for static configuration. However, the above method cannot select the best configuration from many-beam templates, so that the network performance cannot be optimized. And the number of base stations is greatly increased, which causes the method to be low in efficiency. In view of the above, an embodiment of the present invention provides a large-scale antenna beam configuration method. Fig. 3 is a schematic flowchart of a large-scale antenna beam configuration method according to an embodiment of the present invention, as shown in fig. 3, the method includes:
Specifically, the cell to be configured is a cell requiring large-scale antenna beam configuration. In the cell feature, the building feature is used to represent building information in the cell coverage, where the building information may be the height of a single building, the area of a single building, the location of a single building, the density of buildings in the cell coverage, the number of buildings, and the like. The base station characteristics are used to represent configuration information of a base station corresponding to a cell, where the configuration information of the base station may be an altitude of the base station, a direction of the base station, a downward inclination angle or an azimuth angle of an antenna of the base station, and the like. The feature is used to characterize the feature information in the coverage area of the cell, such as the ratio of the total area of the building to the coverage area of the cell, and the ratio of the area of the feature such as a river, lake, etc. to the coverage area of the cell.
Here, the cell feature may be any one of a building feature, a base station feature and a feature of a feature, or may be any two or all of a building feature, a base station feature and a feature of a feature, and this is not particularly limited in the embodiment of the present invention.
Specifically, the beam configuration model is used for selecting and outputting a beam template corresponding to the cell to be configured from a large number of preset beam templates based on the input cell characteristics of the cell to be configured. Here, the preset beam template is a preset number of templates available for large-scale antenna beam configuration. The beam template output by the beam configuration model is selected from a large number of preset beam templates based on the cell characteristics of the cell to be configured, namely the beam template is one of the preset beam templates. The beam template contains preset beam configuration information, and the beam configuration information is used for configuring beam forming, beam scanning and the like of the large-scale antenna.
Before step 320 is executed, the beam configuration model may also be obtained by training in advance, and specifically, the beam configuration model may be obtained by training in the following manner: firstly, collecting sample cell characteristics and sample beam templates of a large number of sample cells; the sample cell is a cell applied during model training, the sample cell characteristic is a cell characteristic of the sample cell, and similarly, the sample cell characteristic also comprises at least one of a building characteristic, a base station characteristic and a ground feature characteristic; the sample beam template is a beam template corresponding to the sample cell, and similarly, the sample beam template is selected from a large number of preset beam templates based on the sample cell characteristics. And training the initial model based on the sample cell characteristics and the sample beam template to obtain a beam configuration model. The initial model may be a single neural network model or a combination of a plurality of neural network models, and the embodiment of the present invention does not specifically limit the type and structure of the initial model.
According to the method provided by the embodiment of the invention, the beam template is obtained by inputting the cell characteristics of the cell to be configured into the beam configuration model, so that the large-scale antenna beam configuration is realized, wherein the application of the beam configuration model can accurately and rapidly match the optimal beam template for the cell to be configured, the defects of poor accuracy and low efficiency of manually selecting the beam template are overcome, the large-scale antenna beam configuration efficiency is improved, the labor cost and the time cost are reduced, and the improvement of the network performance is realized.
Based on the above embodiment, in the method, step 320 further includes:
step 301, a network simulation model is constructed based on the electronic map, the wireless propagation model and the sample base station characteristics of the sample cell.
Specifically, the electronic map includes a coverage area of each sample cell. A Wireless propagation model (Wireless propagation model) is a model designed to accurately characterize Wireless propagation, and is generally used for modeling and simulation of a Wireless channel. The wireless propagation model is composed of various types, such as a Lee model, an Okumura-Hata model, a ray tracing model, and the like. The sample base station characteristics of the sample cell can be used for positioning the sample cell in the electronic map, configuring parameters of a base station simulation model corresponding to the sample cell and the like.
And based on the electronic map, the wireless propagation model and the sample base station characteristics of the sample cell, the construction of the network simulation model can be realized. The network simulation model herein is used to simulate the radio coverage status of a sample cell. It should be noted that the network simulation model may include simulation models of multiple sample cells at the same time, and simulate the wireless coverage states of multiple sample cells at the same time.
Step 302, configuring a sample cell in the network simulation model based on any preset beam template, and acquiring wireless coverage simulation data of the sample cell.
Specifically, after a network simulation model is obtained for any sample cell, any one beam template is selected from a large number of preset beam templates, and the sample cell is configured based on the preset beam template, so that wireless coverage simulation data of the sample cell under the configuration of the preset beam template is obtained. Here, the wireless coverage simulation data is simulation data of a sample cell output by the network simulation model in a coverage area under the preset beam template configuration, and the wireless coverage simulation data may be path loss and beam gain between a base station corresponding to the sample cell and a receiving point, or may be signal field intensity of each receiving point, or the like.
Step 303, based on the wireless coverage simulation data, obtaining a beam performance index of the sample cell under the configuration of the preset beam template.
Specifically, the beam performance index is used to measure the simulated beam performance of the sample cell under the preset beam template configuration, and the higher the beam performance index is, the better the simulated beam performance of the corresponding sample cell under the preset beam template configuration is.
And 304, selecting a preset beam template corresponding to the maximum value of the beam performance index as a sample beam template of the sample cell.
Specifically, for any sample cell, after the beam performance indexes of the sample cell under the configuration of the multiple preset beam templates are obtained, the preset beam template corresponding to the largest beam performance index is selected from the sample cell, that is, the preset beam template capable of realizing the optimal simulation beam performance is used as the sample beam template of the sample cell.
Step 305, training a beam configuration model based on the sample cell characteristics and the sample beam template.
Specifically, after a sample beam template corresponding to each sample cell is obtained, a beam configuration model is trained based on sample cell characteristics of the sample cell and the sample beam template.
According to the method provided by the embodiment of the invention, wireless coverage simulation under the preset beam template is carried out through the network simulation model, so that the sample beam template with the optimal beam performance is obtained, and the accuracy of the output of the beam configuration model is improved.
Based on any of the above embodiments, in this method, step 303 specifically includes: acquiring the number of coverage grids and the number of interference grids which are met by a sample cell under the configuration of the preset beam template based on wireless coverage simulation data; and acquiring the beam performance index based on the requirement of the number of the coverage grids and the number of the interference grids.
Here, the number of grids satisfying the number of coverage grids, that is, the number of grids satisfying the RSRP (Reference Signal Receiving Power) within the coverage area of the sample cell is equal to or greater than a preset RSRP threshold. And when the interference grid number is the adjacent area of the sample cell, the RSRP is greater than or equal to a preset RSRP threshold value, and the difference between the interference grid number and the RSRP of the main cell is less than the grid number of a preset difference value.
After the number of covering grids and the number of interference grids are obtained, the beam performance index can be calculated through the following formula:
the effective grid number satisfies the covering grid number-the interference coefficient;
in the formula, the effective grid number is a beam performance index, the interference coefficient is a preset coefficient, and the value of the interference coefficient is between 0 and 1.
Based on any of the above embodiments, in the method, step 305 specifically includes: and training a beam configuration model by applying a K-nearest neighbor algorithm based on the sample cell characteristics and the sample beam template.
Specifically, cells with similar cell characteristics, such as building distribution, terrain distribution, and engineering parameter data, also typically employ similar beam configurations. Therefore, in the embodiment of the invention, the training of the beam configuration model is performed by applying the K-nearest neighbor algorithm. The K-Nearest Neighbor (KNN) algorithm is a machine learning algorithm. The idea of the algorithm is as follows: if a sample belongs to a certain class in the majority of the k most similar samples in feature space (i.e. the nearest neighbors in feature space), then the sample also belongs to this class. And applying a K-nearest neighbor algorithm to the training of the beam configuration model, so that the beam configuration model compares the input cell characteristics with the pre-stored sample cell characteristics, finds the first K sample cell characteristics which are most similar to the input cell characteristics in the sample cell characteristics, and takes the sample beam template with the largest occurrence frequency in the sample beam templates respectively corresponding to the first K sample cell characteristics as the beam template corresponding to the input cell characteristics.
According to any of the above embodiments, the method further includes, before step 305: acquiring coverage data of a sample cell based on a network simulation model; preprocessing the coverage range data; preprocessing includes de-duplication and/or de-outlier; based on the coverage data, sample cell characteristics are obtained.
Specifically, in a network simulation model constructed based on an electronic map, a wireless propagation model, and sample base station characteristics of a sample cell, coverage data of the sample cell may be obtained. Here, the coverage data of the sample cell includes building feature data, feature data of the ground feature, and feature data of the base station corresponding to the sample cell within the coverage of the sample cell. The building characteristic data and the ground feature characteristic data can be obtained in an electronic map after the coverage range is determined through the wireless propagation model and the sample base station characteristics of the sample cell.
After the coverage data is obtained, the coverage data needs to be preprocessed. In the preprocessing, the repetition value in the coverage data is removed, namely the repetition value is screened out, the abnormal value in the coverage data is removed, namely the abnormal value is screened out, so that the interference caused by the repetition value or the abnormal value in the subsequent model training is avoided, and the model training quality is improved. Further, the method for removing the outlier includes a 3 σ criterion, a simple statistical method, a boxed graph analysis method, and the like, which is not particularly limited in the embodiment of the present invention. When the 3 sigma criterion is applied to remove the abnormal value, each class of values in the coverage range data is assumed to be in normal distribution, 99% of the similar values should be located within 3 standard deviations from the mean value, and if the values exceed the distances, the abnormal values are determined and removed.
After the preprocessing is finished, acquiring the sample cell characteristics based on the coverage data after the preprocessing, and training a beam configuration model based on the sample cell characteristics and the sample beam template.
According to any of the above embodiments, the method further includes, before step 310: acquiring the coverage area of a cell to be configured; and acquiring the building characteristics, the base station characteristics and the ground feature characteristics of the cell to be configured based on the coverage range of the cell to be configured.
Specifically, when a large-scale antenna beam of a cell to be configured needs to be configured, a coverage area of the cell to be configured needs to be obtained first, and building features, base station features, and ground feature features in the coverage area are used as cell features of the cell to be configured.
Further, acquiring the coverage area of the cell to be configured specifically includes: and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
Specifically, an MR (Measurement Report), that is, a Measurement Report reported by a terminal, may determine a coverage area of a cell to be configured according to the MR. For example, the MR is positioned into a specific grid according to an algorithm such as fingerprint library positioning, and the coverage of the cell to be configured is determined according to the cell ID in the MR and the grid ID after positioning.
In addition, the coverage area of the cell to be configured can be determined according to the geographic position of the cell to be configured. For example, the base station of the cell to be configured is located according to the geographical position of the cell to be configured, and a sector is drawn by taking the base station as the center of a circle and taking the preset length as the radius. The preset length of the dense urban area is 280 meters, the preset length of the general urban area is 340 meters, the direction pointed by the center line of the sector is the direction pointed by the direction angle of the base station antenna, and the coverage area of the sector is the coverage area of the cell to be configured.
According to any of the above embodiments, in the method, the building characteristics include at least one of building height information, building area information, and building location information; the base station is characterized by comprising at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position; the feature of the ground feature comprises the area ratio of the covered lower chamber.
The building height information may be a specific height value, or may be a corresponding height type obtained according to the height value, such as a primary height, a secondary height, and the like. The building area information may be a specific area value, or may be a corresponding area type obtained according to the area value, such as a small area, a medium area, a large area, and the like. The building location information may be the longitude and latitude of the building center; the occupancy ratio of the indoor area under the coverage can be obtained by the quotient of the number of grids occupied by the buildings of the cell and the total number of grids of the cell.
Based on any of the above embodiments, in the method, the building height information is a short building, a medium building or a high building, the building area information is a general building or a large building, the building position information is a distance grade between the building and the base station, and the distance grade is one of near, medium and far.
Specifically, the building height information is determined by comparing the building height with preset height intervals corresponding to short buildings, middle buildings and tall buildings. The building area information is determined by comparing the building area value with the area sections respectively corresponding to the general building and the large building which are preset. The building position information is determined by comparing the distance value between the building and the base station with the preset distance value intervals corresponding to the near distance grade, the medium distance grade and the far distance grade.
Based on any of the above embodiments, fig. 4 is a schematic flow chart of a large-scale antenna beam configuration method according to another embodiment of the present invention, as shown in fig. 4, the method includes two parts, where the step above the dotted line is a modeling process, and the step below the dotted line is an output process. The method specifically comprises the following steps:
extracting sample cell characteristics including building characteristics, base station characteristics and ground feature characteristics based on the coverage area of the sample cell, and specifically comprising the following steps:
building characteristics: acquiring building information including height, area and longitude and latitude of a building center in a coverage area of a sample cell;
the base station is characterized in that: acquiring working parameter data of a sample cell, wherein the working parameter data comprises the height of a base station, an antenna direction angle, an electronic downward inclination angle, a mechanical downward inclination angle, transmitting power and longitude and latitude of the base station;
the feature of the ground features: obtaining the indoor area ratio under the coverage of the sample cell, wherein the calculation mode is as follows:
and the indoor area ratio under the coverage of the cell A is equal to the total grid number of the buildings in the cell A/the grid number of all the cells in the cell A.
fig. 5 is a flowchart illustrating a method for obtaining a sample beam template according to an embodiment of the present invention, and as shown in fig. 5, step 404 specifically includes the following steps:
501, creating an electronic map: and creating a project, and importing a special electronic map required by the coverage simulation into the project.
502, configuring a wireless propagation model: and selecting a wireless propagation model in engineering, and editing corresponding parameters of the wireless propagation model.
503, importing base station features and a preset beam template: introducing base station characteristics corresponding to a sample cell required by simulation, wherein the base station characteristics comprise longitude and latitude, station height, azimuth angle, declination angle, transmitting power and the like of a base station; one preset beam template is selected from a large number of preset beam templates, and the preset beam template is guided into the Massive MIMO beam configuration of the simulation base station. A Massive MIMO beam configuration typically includes horizontal and vertical directional patterns of each beam for determining the transmit gain of the beam.
504, wireless coverage simulation: and calculating the path loss and the beam gain between the base station and the receiving points according to the imported model, the engineering parameter data and the like, and calculating the signal field intensity of each receiving point according to the transmitting power.
505, single cell coverage interference data extraction: extracting the number of coverage grids met by each sample cell and the number of interference grids of each sample cell to other sample cells according to the simulation result;
wherein, the total number of grids satisfying the RSRP-105 db in the cell coverage range; the interference grid number is the grid number of the cell which is used as the neighbor cell and has the level RSPR > of-105 db and is within 6db of the level of the main cell.
506, beam performance evaluation: evaluating the beam performance through the effective grid number, and considering that the beam performance under the preset beam template configuration is optimal when the effective grid number of the sample cell under any preset beam template configuration is more; wherein, the effective grid number satisfies the covering grid number-interference coefficient, and the interference coefficient value range is 0-1.
507, determining a sample beam template: and selecting the best beam template, namely the sample beam template, for the sample cell according to the beam performance evaluation result of the sample cell under each preset beam template configuration for subsequent machine learning algorithm training.
fig. 6 is a schematic flow chart of a training method of a beam configuration model according to an embodiment of the present invention, and as shown in fig. 6, the training method of the beam configuration model in step 406 is as follows:
601, data splitting: and splitting the data in the training sample library according to the ratio of 7:3 to respectively obtain a training set and a test set.
602, training a K neighbor model: and training a beam configuration model by applying a K-nearest neighbor algorithm based on the sample cell characteristics and the sample beam template in the training set.
603, model parameter adjustment: after the beam configuration model is trained, inputting the characteristics of the sample cells in the test set into the beam configuration model, comparing the beam model corresponding to the sample cells output by the beam configuration model with the sample beam model, and adjusting the parameters in the beam configuration model according to the comparison result.
And 604, outputting the beam configuration model after model parameter adjustment.
According to the method provided by the embodiment of the invention, the beam template is obtained by inputting the cell characteristics of the cell to be configured into the beam configuration model, so that the large-scale antenna beam configuration is realized, wherein the application of the beam configuration model can accurately and rapidly match the optimal beam template for the cell to be configured, the defects of poor accuracy and low efficiency of manually selecting the beam template are overcome, the large-scale antenna beam configuration efficiency is improved, the labor cost and the time cost are reduced, and the improvement of the network performance is realized.
Based on any of the above embodiments, fig. 7 is a schematic structural diagram of a large-scale antenna beam configuration apparatus according to an embodiment of the present invention, as shown in fig. 7, the apparatus includes a cell characteristic obtaining unit 710, a beam template obtaining unit 720, and a beam configuration unit 730;
the cell characteristic acquiring unit 710 is configured to acquire a cell characteristic of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics;
the beam template obtaining unit 720 is configured to input the cell characteristics of the cell to be configured to a beam configuration model, and obtain a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training;
the beam configuration unit 730 is configured to configure a large-scale antenna beam of the cell to be configured based on the beam template.
According to the device provided by the embodiment of the invention, the beam template is obtained by inputting the cell characteristics of the cell to be configured into the beam configuration model, so that the large-scale antenna beam configuration is realized, wherein the application of the beam configuration model can accurately and rapidly match the optimal beam template for the cell to be configured, the defects of poor accuracy and low efficiency of manually selecting the beam template are overcome, the large-scale antenna beam configuration efficiency is improved, the labor cost and the time cost are reduced, and the improvement of the network performance is realized.
Based on any embodiment above, the apparatus further comprises:
the network simulation model building unit is used for building a network simulation model based on an electronic map, a wireless propagation model and the sample base station characteristics of the sample cell;
a wireless coverage data obtaining unit, configured to configure the sample cell in the network simulation model based on any preset beam template, and obtain wireless coverage simulation data of the sample cell;
a beam performance index obtaining unit, configured to obtain a beam performance index of the sample cell under any preset beam template configuration based on the wireless coverage simulation data;
a sample beam template selecting unit, configured to select the preset beam template corresponding to the maximum value of the beam performance index as the sample beam template of the sample cell;
a model training unit, configured to train the beam configuration model based on the sample cell characteristics and the sample beam template.
Based on any of the above embodiments, in the apparatus, the beam performance index obtaining unit is specifically configured to:
acquiring the number of coverage grids and the number of interference grids which are met by the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
and acquiring the beam performance index based on the number of the coverage grids and the number of the interference grids.
Based on any of the above embodiments, in the apparatus, the model training unit is specifically configured to:
training the beam configuration model using a K-nearest neighbor algorithm based on the sample cell features and the sample beam template.
Based on any of the above embodiments, the apparatus further includes a sample cell feature extraction unit; the sample cell feature extraction unit is specifically configured to:
acquiring coverage data of the sample cell based on the network simulation model;
preprocessing the coverage range data; the preprocessing comprises de-duplication and/or de-outlier;
and acquiring the sample cell characteristics based on the coverage data.
Based on any of the above embodiments, in the apparatus, the cell characteristic obtaining unit 710 specifically includes:
a coverage obtaining subunit, configured to obtain a coverage of the cell to be configured;
and the characteristic obtaining subunit is configured to obtain the building characteristic, the base station characteristic and the feature characteristic of the cell to be configured based on the coverage area of the cell to be configured.
Based on any of the above embodiments, in the apparatus, the coverage area obtaining subunit is specifically configured to:
and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
According to any one of the above embodiments, in the apparatus, the building characteristics include at least one of building height information, building area information, and building location information;
the base station characteristics comprise at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position;
the feature of the feature comprises the area ratio of the covered lower chamber.
Based on any of the above embodiments, in the apparatus, the building height information is a short building, a medium building, or a high building, the building area information is a general building or a large building, the building position information is a distance grade between the building and the base station, and the distance grade is one of near, medium, and far.
Fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 8, the electronic device may include: a processor (processor)801, a communication Interface (Communications Interface)802, a memory (memory)803 and a communication bus 804, wherein the processor 801, the communication Interface 802 and the memory 803 complete communication with each other through the communication bus 804. The processor 801 may invoke a computer program stored on the memory 803 and executable on the processor 801 to perform the massive antenna beam configuration method provided by the above embodiments, for example, including: acquiring cell characteristics of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics; inputting the cell characteristics of the cell to be configured into a beam configuration model, and acquiring a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training; and configuring the large-scale antenna beams of the cell to be configured based on the beam template.
In addition, the logic instructions in the memory 803 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method for configuring a large-scale antenna beam provided in the foregoing embodiments when executed by a processor, and the method includes: acquiring cell characteristics of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics; inputting the cell characteristics of the cell to be configured into a beam configuration model, and acquiring a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training; and configuring the large-scale antenna beams of the cell to be configured based on the beam template.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (20)
1. A method for large-scale antenna beam configuration, comprising:
acquiring cell characteristics of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics;
inputting the cell characteristics into a beam configuration model, and acquiring a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training;
and configuring the large-scale antenna beams of the cell to be configured based on the beam template.
2. The massive antenna beam configuration method according to claim 1, wherein the inputting the cell characteristics into a beam configuration model and obtaining the beam template output by the beam configuration model further comprise:
constructing a network simulation model based on an electronic map, a wireless propagation model and the sample base station characteristics of the sample cell;
configuring the sample cell in the network simulation model based on any preset beam template, and acquiring wireless coverage simulation data of the sample cell;
acquiring a beam performance index of the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
selecting the preset beam template corresponding to the maximum value of the beam performance index as the sample beam template of the sample cell;
training the beam configuration model based on the sample cell features and the sample beam template.
3. The method according to claim 2, wherein the obtaining a beam performance index of the sample cell under any one of the preset beam template configurations based on the wireless coverage simulation data specifically includes:
acquiring the number of coverage grids and the number of interference grids which are met by the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
and acquiring the beam performance index based on the number of the coverage grids and the number of the interference grids.
4. The method according to claim 2, wherein the training the beam configuration model based on the sample cell characteristics and the sample beam template specifically includes:
training the beam configuration model using a K-nearest neighbor algorithm based on the sample cell features and the sample beam template.
5. The massive antenna beam configuration method of claim 2, wherein the training of the beam configuration model based on the sample cell characteristics and the sample beam template further comprises:
acquiring coverage data of the sample cell based on the network simulation model;
preprocessing the coverage range data; the preprocessing comprises de-duplication and/or de-outlier;
and acquiring the sample cell characteristics based on the coverage data.
6. The method according to claim 1, wherein the obtaining the cell characteristics of the cell to be configured specifically includes:
acquiring the coverage area of a cell to be configured;
and acquiring the building characteristic, the base station characteristic and the ground feature characteristic of the cell to be configured based on the coverage range of the cell to be configured.
7. The method according to claim 6, wherein the obtaining the coverage area of the cell to be configured specifically includes:
and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
8. The massive antenna beam configuration method of any of claims 1 to 7, wherein the building characteristics comprise at least one of building height information, building area information, and building location information;
the base station characteristics comprise at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position;
the feature of the feature comprises the area ratio of the covered lower chamber.
9. The method as claimed in claim 8, wherein the building height information is short, medium or high building, the building area information is general or large building, the building location information is a distance level between the building and the base station, and the distance level is one of near, medium and far.
10. An apparatus for massive antenna beam configuration, comprising:
a cell feature acquiring unit, configured to acquire a cell feature of a cell to be configured; the cell characteristics include at least one of building characteristics, base station characteristics, and terrain characteristics;
a beam template obtaining unit, configured to input the cell characteristics into a beam configuration model, and obtain a beam template output by the beam configuration model; the beam configuration model is obtained based on sample cell characteristics of a sample cell and sample beam template training;
and the beam configuration unit is used for configuring the large-scale antenna beam of the cell to be configured based on the beam template.
11. The massive antenna beam configuring device of claim 10, further comprising:
the network simulation model building unit is used for building a network simulation model based on an electronic map, a wireless propagation model and the sample base station characteristics of the sample cell;
a wireless coverage data obtaining unit, configured to configure the sample cell in the network simulation model based on any preset beam template, and obtain wireless coverage simulation data of the sample cell;
a beam performance index obtaining unit, configured to obtain a beam performance index of the sample cell under any preset beam template configuration based on the wireless coverage simulation data;
a sample beam template selecting unit, configured to select the preset beam template corresponding to the maximum value of the beam performance index as the sample beam template of the sample cell;
a model training unit, configured to train the beam configuration model based on the sample cell characteristics and the sample beam template.
12. The massive antenna beam configuration device according to claim 11, wherein the beam performance index obtaining unit is specifically configured to:
acquiring the number of coverage grids and the number of interference grids which are met by the sample cell under the configuration of any preset beam template based on the wireless coverage simulation data;
and acquiring the beam performance index based on the number of the coverage grids and the number of the interference grids.
13. The massive antenna beam configuration device according to claim 11, wherein the model training unit is specifically configured to:
training the beam configuration model using a K-nearest neighbor algorithm based on the sample cell features and the sample beam template.
14. The massive antenna beam configuration device according to claim 11, further comprising a sample cell feature extraction unit; the sample cell feature extraction unit is configured to:
acquiring coverage data of the sample cell based on the network simulation model;
preprocessing the coverage range data; the preprocessing comprises de-duplication and/or de-outlier;
and acquiring the sample cell characteristics based on the coverage data.
15. The massive antenna beam configuration device according to claim 10, wherein the cell characteristic obtaining unit comprises:
a coverage obtaining subunit, configured to obtain a coverage of the cell to be configured;
and the characteristic obtaining subunit is configured to obtain the building characteristic, the base station characteristic and the feature characteristic of the cell to be configured based on the coverage area of the cell to be configured.
16. The massive antenna beam configuration device according to claim 15, wherein the coverage area obtaining subunit is specifically configured to:
and acquiring the coverage area of the cell to be configured based on the measurement report and/or the geographical position of the cell to be configured.
17. The massive antenna beam configuring device of any one of claims 10 to 16, wherein the building characteristics comprise at least one of building height information, building area information, and building location information;
the base station characteristics comprise at least one of base station height, base station antenna direction angle, base station antenna electronic downward inclination angle, base station antenna mechanical downward inclination angle, base station antenna transmitting power and base station position;
the feature of the feature comprises the area ratio of the covered lower chamber.
18. The massive antenna beam configuration device according to claim 17, wherein the building height information is a short building, a medium building or a high building, the building area information is a general building or a large building, the building location information is a distance grade between a building and a base station, and the distance grade is one of near, medium and far.
19. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of massive antenna beam configuration according to any of claims 1 to 9.
20. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the method of massive antenna beam configuration according to any one of claims 1 to 9.
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