CN113015192A - Antenna weight determination method, device, equipment and storage medium - Google Patents
Antenna weight determination method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides an antenna weight determination method, device, equipment and storage medium. The method comprises the following steps: acquiring 4G MDT data and 5G MR of a plurality of UE accessing a target 5G cell and an antenna file of a target antenna corresponding to the target 5G cell, wherein the 4G MDT data comprises UE position information, the 5G MR comprises SSB-RSRP, and the antenna file comprises a plurality of antenna weights; dividing the coverage area of a target 5G cell into a plurality of sub-coverage areas, and respectively allocating UE to a first sub-coverage area corresponding to UE position information of the UE; determining a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the first sub-coverage area, determining a first antenna weight corresponding to the first coverage area to be optimized, and generating a target antenna weight set; and calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction meets a preset gain condition in the target antenna weight set as the target antenna weight. Thus, the antenna weight determination efficiency can be improved.
Description
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for determining an antenna weight.
Background
At present, a large-scale Multiple-Input Multiple-output (Massive MIMO) antenna adopted by a 5G cell generally adopts a default antenna weight at a network access stage, a beam coverage effect of the 5G cell is poor, a target antenna weight with a better beam coverage effect is often required to be determined at a later stage, and then the antenna weight is adjusted to the target antenna weight.
However, the conventional antenna weight determination method needs to perform a full traversal on the antenna weights of the antennas to determine the target antenna weights. With the expansion of the antenna weight range of the 5G antenna, the complexity of the full traversal of the antenna weight is exponentially increased, and the determination efficiency of the antenna weight is low.
Disclosure of Invention
The embodiment of the application provides an antenna weight determination method, device, equipment and storage medium, and can improve the antenna weight determination efficiency.
In a first aspect, an embodiment of the present application provides an antenna weight determining method, where the method includes:
acquiring 4G Minimization Drive Test (MDT) data and a 5G Measurement Report (Measurement Report, MR) of a plurality of User Equipment (UE) accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell, wherein the 4G MDT data comprises UE position information, the 5G MR comprises Synchronization Signal-Reference Signal Received Power (SSB-RSRP), and the antenna file comprises a plurality of antenna weights;
dividing a coverage area of a target 5G cell into a plurality of sub-coverage areas, and respectively allocating a plurality of UEs to first sub-coverage areas corresponding to UE position information of the UEs in the plurality of sub-coverage areas;
determining a first coverage area to be optimized in the coverage area according to SSB-RSRP of UE in the first sub-coverage areas, determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights;
and calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction in the target antenna weight set meets a preset gain condition as the target antenna weight.
In some implementations of the first aspect, determining the first area of coverage to be optimized from the SSB-RSRPs of UEs in the plurality of first sub-coverage areas comprises:
determining a first sub-coverage area, in the plurality of first sub-coverage areas, of which the SSB-RSRP of the UE meets a preset weak coverage condition as a weak coverage area;
and clustering and shaping the weak coverage area according to a clustering algorithm and a shaping algorithm to obtain a first coverage area to be optimized.
In some implementations of the first aspect, the clustering algorithm comprises a DBSCAN algorithm and the shaping algorithm comprises an Alpha Shape algorithm.
In some implementations of the first aspect, calculating the antenna gain fraction for each antenna weight in the set of target antenna weights comprises:
calculating SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set;
and calculating the antenna gain fraction of each antenna weight in the target antenna weight set according to the SSB-RSRP of the UE in each sub-coverage area corresponding to each antenna weight in the target antenna weight set.
In some implementation manners of the first aspect, determining, as the target antenna weight, an antenna weight in the target antenna weight set for which the antenna gain fraction satisfies a preset gain condition includes:
and determining the antenna weight with the maximum antenna gain fraction in the target antenna weight set as the target antenna weight.
In some implementation manners of the first aspect, after determining, as the target antenna weight, an antenna weight in the target antenna weight set whose antenna gain fraction satisfies a preset gain condition, the method further includes:
and gradually adjusting the antenna weight value currently configured by the target antenna to the target antenna weight value according to a preset stepping algorithm.
In some implementations of the first aspect, the method further comprises:
acquiring a three-dimensional electronic map of a coverage area and 5G drive test data of drive test equipment accessed to a target 5G cell, wherein the 5G drive test data comprises sampling point position information and sampling point SSB-RSRP;
extracting feature information of buildings in a coverage area from the three-dimensional electronic map, and determining a plurality of second antenna weights corresponding to the feature information from the plurality of antenna weights;
determining a second coverage area to be optimized in the coverage area according to the sampling point position information and the sampling point SSB-RSRP, and determining a plurality of third antenna weights corresponding to the second coverage area to be optimized from the plurality of antenna weights;
generating a target antenna weight set comprising a plurality of first antenna weights, comprising:
and taking a union set of the multiple first antenna weights, the multiple second antenna weights, the multiple third antenna weights and the antenna weights currently configured by the target antenna to obtain a target antenna weight set.
In a second aspect, an embodiment of the present application provides an antenna weight determining apparatus, where the apparatus includes:
the system comprises an acquisition module and an antenna file acquisition module, wherein the acquisition module is used for acquiring 4G MDT data and 5G MR of a plurality of UE (user equipment) accessed to a target 5G cell and an antenna file of a target antenna corresponding to the target 5G cell, the 4G MDT data comprises UE position information, the 5G MR comprises SSB-RSRP, and the antenna file comprises a plurality of antenna weights;
the dividing module is used for dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas and respectively allocating a plurality of UEs to first sub-coverage areas corresponding to UE position information of the UEs in the plurality of sub-coverage areas;
a determining module, configured to determine a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the multiple first sub-coverage areas, determine multiple first antenna weights corresponding to the first coverage area to be optimized from the multiple antenna weights, and generate a target antenna weight set including the multiple first antenna weights;
and the calculation module is used for calculating the antenna gain fraction of each antenna weight in the target antenna weight set and determining the antenna gain fraction meeting the preset gain condition in the target antenna weight set as the target antenna weight.
In some implementations of the second aspect, the determining module includes:
a first determining unit, configured to determine, as a weak coverage area, a first sub-coverage area in the plurality of first sub-coverage areas, where an SSB-RSRP of the UE meets a preset weak coverage condition;
and the clustering unit is used for clustering and shaping the weak coverage area according to a clustering algorithm and a shaping algorithm to obtain a first coverage area to be optimized.
In some implementations of the second aspect, the clustering algorithm comprises a DBSCAN algorithm and the shaping algorithm comprises an Alpha Shape algorithm.
In some implementations of the second aspect, the computing module includes:
the calculating unit is used for calculating the SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set;
and the calculating unit is further configured to calculate an antenna gain fraction of each antenna weight in the target antenna weight set according to the SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set.
In some implementations of the second aspect, the determining module includes:
and the second determining unit is used for determining the antenna weight with the largest antenna gain fraction in the target antenna weight set as the target antenna weight.
In some implementations of the second aspect, the apparatus further comprises:
and the adjusting module is used for gradually adjusting the antenna weight currently configured by the target antenna to the target antenna weight according to a preset stepping algorithm after determining that the antenna weight of which the antenna gain fraction meets the preset gain condition in the target antenna weight set is the target antenna weight.
In some realizations of the second aspect, the obtaining module is further configured to obtain a three-dimensional electronic map of a coverage area and 5G drive test data of a drive test device accessing the target 5G cell, where the 5G drive test data includes sampling point position information and sampling point SSB-RSRP;
the device also includes: the extracting module is used for extracting the feature information of the buildings in the coverage area from the three-dimensional electronic map and determining a plurality of second antenna weights corresponding to the feature information from the plurality of antenna weights;
the determining module is further configured to determine a second coverage area to be optimized in the coverage area according to the sampling point position information and the sampling point SSB-RSRP, and determine a plurality of third antenna weights corresponding to the second coverage area to be optimized from the plurality of antenna weights;
the determining module comprises: and the union set unit is used for taking a union set of the multiple first antenna weights, the multiple second antenna weights, the multiple third antenna weights and the antenna weights currently configured by the target antenna to obtain a target antenna weight set.
In a third aspect, an embodiment of the present application provides an antenna weight determining device, where the device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, may implement the method for determining antenna weights as described in the first aspect or any of the realizable manners of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the method for determining antenna weights in the first aspect or any of the realizations of the first aspect is implemented.
The method, the device, the equipment and the storage medium for determining the antenna weight can acquire the 4G MDT data and the 5G MR of a plurality of UE accessing a target 5G cell and the antenna file of a target antenna corresponding to the target 5G cell. The 4G MDT data comprises UE position information, the 5G MR comprises SSB-RSRP, and the antenna file comprises a plurality of antenna weights. And then dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas, and respectively allocating the plurality of UEs to first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas. And then, according to SSB-RSRP of the UE in the plurality of first sub-coverage areas, determining a first coverage area to be optimized in the coverage area, determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights. And then calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction meets a preset gain condition in the target antenna weight set as the target antenna weight. Therefore, only the antennas in the target antenna weight set can be traversed, the traversal complexity is greatly reduced, and the antenna weight determination efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an architecture of an antenna weight determination system according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for determining antenna weights according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a 4G MDT data and a 5G MR association provided by an embodiment of the present application;
fig. 4 is a schematic diagram of a weak coverage area distribution provided by an embodiment of the present application;
fig. 5 is a schematic diagram of a first coverage area to be optimized according to an embodiment of the present application;
fig. 6 is a schematic diagram of a first coverage area level to be optimized according to an embodiment of the present application;
fig. 7 is a schematic diagram of a vertical plane of a first coverage area to be optimized according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a building level in a coverage area provided by an embodiment of the present application;
FIG. 9 is a schematic illustration of a vertical plane of a building in a coverage area provided by an embodiment of the present application;
fig. 10 is a schematic diagram of a second coverage area to be optimized according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of an antenna weight determining apparatus according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of an antenna weight determining device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and do not limit the application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
To solve the problems in the background art, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for determining an antenna weight, which can obtain 4G MDT data and 5G MR of a plurality of UEs accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell. The 4G MDT data comprises UE position information, the 5G MR comprises SSB-RSRP, and the antenna file comprises a plurality of antenna weights. And then dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas, and respectively allocating the plurality of UEs to first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas. And then, according to SSB-RSRP of the UE in the plurality of first sub-coverage areas, determining a first coverage area to be optimized in the coverage area, determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights. And then calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction meets a preset gain condition in the target antenna weight set as the target antenna weight. Therefore, only the antennas in the target antenna weight set can be traversed, the traversal complexity is greatly reduced, and the antenna weight determination efficiency is improved.
The method, apparatus, device and storage medium for determining antenna weight according to the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of an antenna weight determining system according to an embodiment of the present application, and as shown in fig. 1, the antenna weight determining system may include an electronic device 110 and a server 120, which is not limited herein in the embodiment of the present application.
The electronic device 110 may be a mobile electronic device or a non-mobile electronic device. For example, the Mobile electronic device may be a Mobile phone, a tablet Computer, a notebook Computer, a palmtop Computer, an Ultra-Mobile Personal Computer (UMPC), and the like, and the non-Mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), and the like.
The server 120 may be a data server in which various data for antenna weight determination are stored in aggregate. Alternatively, the server 120 may be a single server, a cluster of servers, or a cloud server, among others.
As shown in fig. 1, a communication connection exists between the electronic device 110 and the server 120. Alternatively, the communication means may be wired communication or wireless communication. Illustratively, the electronic device 110 and the server 120 may communicate over a network cable.
As an example, the antenna weight determination system may be applied to a scenario of antenna weight determination for 5G antennas. Referring to fig. 1, the electronic device 110 may obtain, from the server 120, 4G MDT data and 5G MRs of a plurality of UEs accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell. The 4G MDT data includes UE location information, such as latitude and longitude, altitude, and the like, the 5G MR includes SSB-RSRP, and the antenna file includes a plurality of antenna weights, such as a total number of antenna weights of a target antenna provided by a manufacturer. Then, the coverage area of the target 5G cell is divided into a plurality of sub-coverage areas, for example, a three-dimensional grid of 5m × 5m, and the plurality of UEs are respectively allocated to the first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas, that is, any UE is allocated to the sub-coverage area where the UE is located. And then, according to the SSB-RSRP of the UE in each first sub-coverage area, determining a first coverage area to be optimized in the coverage area, namely, an area with poor coverage effect, and determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, namely, the antenna weights for optimizing the coverage effect of the first coverage area to be optimized, and generating a target antenna weight set comprising the plurality of first antenna weights. And then calculating the antenna gain fraction of each antenna weight in the target antenna weight set, namely the effect fraction of the antenna weight after the antenna is configured, and determining the antenna weight of which the antenna gain fraction meets the preset gain condition in the target antenna weight set as the target antenna weight. Therefore, only the antennas in the target antenna weight set can be traversed, the traversal complexity is greatly reduced, and the antenna weight determination efficiency is improved.
The method for determining the antenna weight provided by the embodiment of the present application will be described below. The main body of the antenna weight determining method may be the electronic device 110 in the antenna weight determining system shown in fig. 1, or a module in the electronic device 110.
Fig. 2 is a schematic flowchart of an antenna weight determining method provided in an embodiment of the present application, and as shown in fig. 2, the antenna weight determining method may include the following steps:
s210, acquiring 4G MDT data and 5G MR of a plurality of UE accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell.
The target 5G cell is a New Radio (NR) cell in the current network, the connection state of the UE is dual connection, the 4G MDT data includes UE location information, such as longitude and latitude, altitude, and the like, the 5G MR includes SSB-RSRP, the antenna file includes a plurality of antenna weights, such as a total number of antenna weights of the target antenna provided by the manufacturer, and each antenna weight includes a horizontal lobe angle, a vertical lobe angle, an electronic downtilt angle, and an electronic azimuth angle.
Taking a 5G antenna with a target antenna of 64TR as an example, the antenna file may include 17 groups of typical broadcast beam weights, 12 types of adjustable electrical downtilts ranging from (-2 ° - +9 °) and 95 types of adjustable electronic azimuth angles ranging from (-47 ° - +47 °), for a total of 5091 types of antenna weights.
As an example, the 4G GMDT data and the 5G MR of the UE may be obtained by acquiring southbound data by synchronously starting the 4G side proactive _ SgNB _ ADD and the 5G side block _ INFORMATION measurement events. As shown in fig. 3, the 4G MDT data may include longitude and latitude, altitude, and MeNBUEX2APID, and the 5G MR may include Angle of Arrival (AOA), Timing Advance (TA), SgNBUEX2APID, and SSB-RSRP. The 4G MDT and 5G MR data of the same UE can be associated by using the association relationship between the MeNBUEX2APID in the 4G MDT data and the SgNBUEX2APID in the 5G MR data, so that more comprehensive information of the UE can be obtained.
S220, dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas, and respectively allocating the plurality of UEs to first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas.
In particular, the coverage area of the target 5G cell may be divided into sub-coverage areas of a specified size, for example a three-dimensional solid grid of 5m x 5 m. And then respectively allocating the plurality of UEs to a first sub-coverage area corresponding to the UE position information of the UEs in the plurality of sub-coverage areas. For example, any one UE is assigned to the three-dimensional stereoscopic grid in which it is located.
And S230, determining a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the plurality of first sub-coverage areas, determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights.
In one embodiment, the first sub-coverage area, of the plurality of first sub-coverage areas, where the SSB-RSRP of the UE meets the preset weak coverage condition may be determined to be a weak coverage area. Wherein, the presetting of the weak coverage condition may include: UEs in the first sub-coverage area with SB-RSRP higher than-110 dbm account for less than 80% of all UEs in the first sub-coverage area. Illustratively, there are 100 UEs in the first sub-coverage area a, but only 60 UEs have a SB-RSRP higher than-110 dbm, the first sub-coverage area a would be determined to be a weak coverage area. For example, the weak coverage areas may be distributed as shown in fig. 4, wherein a three-dimensional stereoscopic grid in which the UE with a border around is located is the weak coverage area.
And then clustering and shaping the weak coverage area according to a clustering algorithm and a shaping algorithm to obtain a first coverage area to be optimized. For example, the first coverage area to be optimized may be as shown in fig. 5. As an example, the DBSCAN clustering algorithm may be used to cluster the weak coverage areas to obtain clustered areas, and the Alpha Shape algorithm is used to extract the boundaries of the clustered areas to obtain the first coverage area to be optimized, i.e. the area with poor coverage effect. Among them, the DBSCAN algorithm is a relatively representative density-based clustering algorithm, and unlike the partitioning and hierarchical clustering method, it defines clusters as the maximum set of density-connected points, can partition areas with sufficiently high density into clusters, and can find clusters of arbitrary shapes in a spatial database of noise. Therefore, the area to be optimized can be quickly determined through a data mining algorithm, and the processing efficiency is improved.
In one embodiment, angular information of the first to-be-optimized coverage area with respect to the target antenna may be calculated. The angle information may include a horizontal lobe optimization angle, i.e., a maximum angle of lobe horizontal direction adjustment, a vertical lobe optimization angle, i.e., a maximum angle of lobe vertical direction adjustment, an electronic azimuth optimization angle, i.e., a maximum angle of electronic azimuth adjustment, and an electronic downtilt optimization angle, i.e., a maximum angle of electronic downtilt adjustment. Referring to fig. 5, 6 and 7, α is the horizontal lobe optimization angle and β is the vertical lobe optimization angle. Azimuth, not shown in the figure, is an electron Azimuth optimized angle, and downilt is an electron Downtilt optimized angle.
And then determining a plurality of antenna weights corresponding to the angle information from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights by taking the plurality of antenna weights corresponding to the angle information as the first antenna weights.
For example, Pattern _ cov is a target antenna weight set, Pattern _ cov is { Pattern _ i, i is 0, 1, 2, …, n } { SCENARIO _ i, downlink _ i, Azimuth _ i, i is 0, 1, 2, …, n } { { α _ i, β _ i }, downlink _ i, Azimuth _ i, i is 0, 1, 2, …, n }, where α _ i represents a horizontal lobe angle in the ith first antenna weight, β _ i represents a vertical lobe angle in the ith first antenna weight, downntil _ i represents an electronic down tilt angle in the ith first antenna weight, Azimuth _ i represents an electronic Azimuth angle in the ith first antenna weight, and n represents the number of first antenna weights.
S240, calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna gain fraction satisfying the preset gain condition in the target antenna weight set as the target antenna weight.
In one embodiment, the SSB-RSRP of the UE in each sub-coverage area corresponding to each antenna weight in the target antenna weight set may be calculated; and accurately calculating the antenna gain fraction of each antenna weight in the target antenna weight set according to the SSB-RSRP of the UE in each sub-coverage area corresponding to each antenna weight in the target antenna weight set.
Illustratively, the SSB-RSRP in the 5G MR of the UE in each sub-coverage area when each antenna weight in the set of target antenna weights acts on the target antenna may be predicted, and the antenna gain fraction for that antenna weight may be calculated from the predicted SSB-RSRP in the 5G MR of the UE in each sub-coverage area. Alternatively, the antenna gain fraction may be calculated according to a gain calculation formula. Wherein, the gain calculation formula can be as follows:
where W represents the antenna gain fraction, n represents the number of sub-coverage areas in the cell coverage, k represents a weighting factor coefficient, which may be preset, and cov represents the SSB-RSRP corresponding to the sub-coverage area, for example, the average of the SSB-RSRPs of all UEs in the sub-coverage area. m represents the weight of the fault, and if the fault affects the service, m can be 0, which represents that the fault is preferentially processed at this time.
In one embodiment, the antenna weight with the largest antenna gain fraction in the target antenna weight set may be determined as the target antenna weight. Therefore, the target antenna weight with the optimal coverage effect can be determined for subsequent target antenna adjustment.
In this embodiment of the present application, a coverage area of a target 5G cell may be divided into a plurality of sub-coverage areas, and a plurality of UEs are respectively allocated to a first sub-coverage area corresponding to UE location information in 4G MDT data of the UEs in the plurality of sub-coverage areas. And then, according to SSB-RSRP in the 5G MR of the UE in the multiple first sub-coverage areas, determining a first coverage area to be optimized in the coverage area, determining multiple first antenna weights corresponding to the first coverage area to be optimized from the multiple antenna weights, and generating a target antenna weight set comprising the multiple first antenna weights. And then calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction meets a preset gain condition in the target antenna weight set as the target antenna weight. Therefore, the distribution of the UE in the coverage area can be obtained based on the 4GMDT data and the 5G MR, the target antenna weight set is determined based on the distribution, and only the antennas in the target antenna weight set are traversed, so that the traversing complexity is reduced, and the antenna weight determination efficiency is improved.
In an embodiment, after determining, as the target antenna weight, the antenna weight whose antenna gain fraction satisfies the preset gain condition in the target antenna weight set, the method for determining an antenna weight may further include:
and gradually adjusting the antenna weight value currently configured by the target antenna to the target antenna weight value according to a preset stepping algorithm. Therefore, the target antenna weight can be iteratively approached, and the adverse effect of the adjustment of the antenna weight on the network performance is greatly reduced.
Taking the 5G antenna with the target antenna of 64TR as an example, the adjustment process can be as follows:
firstly: the horizontal 3db lobe angle includes six levels 110, 105, 90, 65, 45, 25, and the vertical 3db lobe angle includes three levels 6, 12, 25, providing that the lobe angle does not adjust more than one level at a time.
Secondly, the method comprises the following steps: the adjustable step number of the electronic downward inclination angle corresponds to different coverage scenes and has three ranges of {1, 7 and 12}, and each adjustment of the electronic downward inclination angle is regulated not to exceed N/2. Wherein N is the maximum adjustment angle of the electronic downtilt of the target antenna weight. The judgment basis of the digital azimuth angle adjustment is the user aggregative analysis, and users are distributed in an obvious aggregation shape and are generally adjusted in place at one time.
Double-layer iteration: the process of specifying and calculating the weight of the target antenna is called large iteration; the process of adjusting the antenna weight by adopting a preset stepping algorithm according to the target weight set is small iteration. The large iteration process is re-triggered only if the antenna negative gain fraction occurs during the small iteration adjustment process. And if the positive gain fraction of the antenna is always in the small iteration process, continuously adjusting according to the existing principle until the weight of the target antenna.
In one embodiment, the method for determining antenna weights may further include:
and acquiring a three-dimensional electronic map of a coverage area and 5G drive test data of drive test equipment accessed to the target 5G cell. The 5G drive test data comprises sampling point position information, such as longitude and latitude, and a sampling point SSB-RSRP.
And then extracting characteristic information of buildings in the coverage area, such as longitude and latitude, altitude and the like of each vertex of the buildings, from the three-dimensional electronic map, and determining a plurality of second antenna weights corresponding to the characteristic information from the plurality of antenna weights.
Specifically, the angle information of the target antenna corresponding to the characteristic information may be determined. The angle information may include a horizontal lobe optimization angle and a vertical lobe optimization angle, among others. Referring to fig. 8 and 9, α 0 is a horizontal lobe optimization angle corresponding to the characteristic information, and β 0 is a vertical lobe optimization angle corresponding to the characteristic information. And then determining a plurality of antenna weights corresponding to the angle information from the plurality of antenna weights, and taking the plurality of antenna weights corresponding to the angle information as a second antenna weight.
And meanwhile, determining a second coverage area to be optimized in the coverage area according to the sampling point position information and the sampling point SSB-RSRP, and determining a plurality of third antenna weights corresponding to the second coverage area to be optimized from the plurality of antenna weights.
In particular, angular information of the second area to be optimized of coverage with respect to the target antenna may be calculated. Wherein the angle information may include a horizontal lobe optimization angle. Referring to fig. 10, α 1 is the horizontal lobe optimization angle in the angular information of the second coverage area to be optimized with respect to the target antenna. And then determining a plurality of antenna weights corresponding to the angle information from the plurality of antenna weights, and taking the plurality of antenna weights corresponding to the angle information as a third antenna weight.
And taking a union set of the multiple first antenna weights, the multiple second antenna weights, the multiple third antenna weights and the antenna weights currently configured by the target antenna to obtain a target antenna weight set. Therefore, more dimensionalities of antenna weights can be introduced into the target antenna weight set, and the accuracy of determining the target antenna weights is improved.
The following describes the method for determining antenna weights in detail with reference to a specific example, specifically as follows:
The base station configuration data includes 5G frequency band data, power data, and the like.
The measurement events used to obtain the 4G MDT data are M1 and PRIVATE _ SgNB _ ADD, defined as follows:
m1: RSRP, RSRQ, measured and reported by UE;
PRIVATE _ SgNB _ ADD, PRIVATE secondary ADD event.
The measurement events used to acquire the 5G MR are:
PUBLIC_INFORMATION；
PERIOD_INTER_RAT_MEASUREMENT；
PERIOD_INTRA_FREQ_MEASUREMENT；
PERIOD_PRIVATE_UE_MEASUREMENT。
and 2, extracting characteristic information of buildings in the coverage area from the three-dimensional electronic map according to the planning data, the work parameter data and the base station configuration data. And determining an antenna weight value set Pattern _ A corresponding to the characteristic information from a plurality of antenna weight values in the antenna file, wherein Pattern _ A comprises a plurality of second antenna weight values.
And 3, extracting the longitude and latitude of a sampling point in the coverage area, the SSB-RSRP corresponding to a target 5G cell at the sampling point and the SSB-RSRP corresponding to an adjacent cell from the 5G drive test data, determining a second coverage area to be optimized in the coverage area according to the information, and determining a target antenna weight set Pattern _ B corresponding to the second coverage area to be optimized from a plurality of antenna weights, wherein the Pattern _ B comprises a plurality of third antenna weights.
And step 4, taking a union set of the Pattern _ A, Pattern _ B and the antenna weight currently configured by the target antenna to obtain an antenna weight set Pattern _ cfg.
And step 5, dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas, and respectively allocating the plurality of UEs to first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas.
And step 6, determining a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the plurality of first sub-coverage areas, and determining an antenna weight set Pattern _ cov corresponding to the first coverage area to be optimized from the plurality of antenna weights. The Pattern _ cov represents an antenna weight set corresponding to a coverage dimension, and includes a plurality of first antenna weights.
Considering the multi-dimensional comprehensive optimization of the coverage area, steps similar to the determination step of Pattern _ cov may be performed to obtain an antenna weight set of corresponding dimensions of the target antenna, such as dimensions of performance, overlapping coverage, and the like. And taking a union set of the antenna weight sets of corresponding dimensionalities to obtain a target antenna weight set. The target antenna weight set may be Pattern _ tgt, which is specifically as follows:
Pattern_tgt＝ Pattern_cfg∪Pattern_cov∪Pattern_pre∪Pattern_ovr (2)
the Pattern _ pre represents an antenna weight set corresponding to a performance dimension, and the Pattern _ ovr represents an antenna weight set corresponding to an overlapping coverage dimension.
And 7, predicting the 5G MR of the UE in each sub-coverage area when each antenna weight in the target antenna weight set acts on the target antenna. The 5G MR may include, among other things, SSB-RSRP and performance parameters such as packet loss rate. And calculating the overlapping coverage parameter of each sub-coverage area according to the SSB-RSRP of the UE in each sub-coverage area. And aiming at any one antenna weight, calculating the antenna gain fraction of the antenna weight according to the overlapping coverage parameter of each sub-coverage area corresponding to the antenna weight and the SSB-RSRP and performance parameters of the UE in each sub-coverage area. Alternatively, the antenna gain fraction may be calculated according to a gain calculation formula. Wherein, the gain calculation formula can be as follows:
where W represents an antenna gain fraction, n represents the number of sub-coverage areas in the cell coverage, k, i, j represent weighting factor coefficients, which may be preset, cov represents SSB-RSRP corresponding to a sub-coverage area, such as an average of SSB-RSRP of all UEs in the sub-coverage area, pre represents a performance parameter corresponding to a sub-coverage area, such as an average of performance parameters of all UEs in the sub-coverage area, ovr represents an overlapping coverage parameter of a sub-coverage area, and m represents a failure weight.
And 8, determining the antenna weight with the maximum antenna gain fraction in the target antenna weight set as the target antenna weight. As shown in table 1, H110_ v6_ tilt0_ ath3 with an antenna gain fraction of 9.10 may be selected as the target antenna weight.
TABLE 1
| Target cell name | Original weight value | New weight value | Fractional antenna gain |
| XX paper mill-0 | H105_v6_tilt0_ath0 | H90_v12_tilt0_ath0 | 6.03 |
| XX paper mill-0 | H105_v6_tilt0_ath0 | H110_v6_tilt0_ath3 | 9.10 |
| XX paper mill-0 | H105_v6_tilt0_ath0 | H90_v6_tilt2_ath0 | 7.08 |
And 9, gradually adjusting the antenna weight value currently configured by the target antenna to the target antenna weight value according to a preset stepping algorithm.
Based on the antenna weight determining method provided by the embodiment of the present application, an embodiment of the present application further provides an antenna weight determining apparatus, and as shown in fig. 11, the antenna weight determining apparatus 1100 may include:
an obtaining module 1110, configured to obtain 4G MDT data and 5G MRs of multiple UEs accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell. The 4GMDT data comprises UE position information, the 5G MR comprises SSB-RSRP, and the antenna file comprises a plurality of antenna weights.
A dividing module 1120, configured to divide the coverage area of the target 5G cell into multiple sub-coverage areas, and allocate multiple UEs to first sub-coverage areas corresponding to UE location information of the UEs in the multiple sub-coverage areas, respectively.
A determining module 1130, configured to determine a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the multiple first sub-coverage areas, determine multiple first antenna weights corresponding to the first coverage area to be optimized from the multiple antenna weights, and generate a target antenna weight set including the multiple first antenna weights.
The calculating module 1140 is configured to calculate an antenna gain score of each antenna weight in the target antenna weight set, and determine an antenna weight in the target antenna weight set, where the antenna gain score satisfies a predetermined gain condition, as the target antenna weight.
In one embodiment, the determination module 1130 includes:
and a first determining unit, configured to determine, as a weak coverage area, a first sub-coverage area in the plurality of first sub-coverage areas, where the SSB-RSRP of the UE meets a preset weak coverage condition.
And the clustering unit is used for clustering and shaping the weak coverage area according to a clustering algorithm and a shaping algorithm to obtain a first coverage area to be optimized.
In one embodiment, the clustering algorithm comprises a DBSCAN algorithm and the shaping algorithm comprises an Alpha Shape algorithm.
In one embodiment, the calculation module 1140 includes:
the calculating unit is used for calculating the SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set;
and the calculating unit is further configured to calculate an antenna gain fraction of each antenna weight in the target antenna weight set according to the SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set.
In one embodiment, the determination module 1130 includes:
and the second determining unit is used for determining the antenna weight with the largest antenna gain fraction in the target antenna weight set as the target antenna weight.
In one embodiment, the antenna weight determining apparatus 1100 further includes:
and the adjusting module is used for gradually adjusting the antenna weight currently configured by the target antenna to the target antenna weight according to a preset stepping algorithm after determining that the antenna weight of which the antenna gain fraction meets the preset gain condition in the target antenna weight set is the target antenna weight.
In one embodiment, the obtaining module 1110 is further configured to obtain a three-dimensional electronic map of a coverage area and 5G drive test data of a drive test device accessing the target 5G cell. The 5G drive test data comprises sampling point position information and sampling points SSB-RSRP.
The antenna weight determining apparatus 1100 further includes: and the extraction module is used for extracting the characteristic information of the buildings in the coverage area from the three-dimensional electronic map and determining a plurality of second antenna weights corresponding to the characteristic information from the plurality of antenna weights.
The determining module 1130 is further configured to determine a second coverage area to be optimized in the coverage area according to the sampling point position information and the sampling point SSB-RSRP, and determine a plurality of third antenna weights corresponding to the second coverage area to be optimized from the plurality of antenna weights.
The determination module 1130 includes: and the union set unit is used for taking a union set of the multiple first antenna weights, the multiple second antenna weights, the multiple third antenna weights and the antenna weights currently configured by the target antenna to obtain a target antenna weight set.
It can be understood that each module/unit in the antenna weight determining apparatus 1100 shown in fig. 11 has a function of implementing each step in the antenna weight determining method provided in the embodiment of the present application, and can achieve the corresponding technical effect, and for brevity, no further description is provided here.
Fig. 12 is a schematic structural diagram of an antenna weight determining device according to an embodiment of the present application. As shown in fig. 12, the antenna weight determination device 1200 in this embodiment includes an input device 1201, an input interface 1202, a central processing unit 1203, a memory 1204, an output interface 1205, and an output device 1206. The input interface 1202, the central processing unit 1203, the memory 1204, and the output interface 1205 are connected to each other through a bus 1210, and the input device 1201 and the output device 1206 are connected to the bus 1210 through the input interface 1202 and the output interface 1205, respectively, and further connected to other components of the antenna weight determination device 1200.
Specifically, the input device 1201 receives input information from the outside and transmits the input information to the central processor 1203 via the input interface 1202; the central processor 1203 processes the input information based on computer-executable instructions stored in the memory 1204 to generate output information, temporarily or permanently stores the output information in the memory 1204, and then transmits the output information to the output device 1206 via the output interface 1205; the output device 1206 outputs the output information to the outside of the antenna weight determination device 1200 for use by the user.
In some embodiments, the antenna weight determination apparatus 1200 shown in fig. 12 includes: a memory 1204 for storing programs; the processor 1203 is configured to run a program stored in the memory, so as to implement the method for determining the antenna weight provided in the embodiment of the present application.
Embodiments of the present application further provide a computer-readable storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement the method for determining antenna weights provided by the embodiments of the present application.
It should be clear that each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments may be referred to each other, and for brevity, the description is omitted. The present application is not limited to the specific configurations and processes described above and shown in the figures. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic Circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuits, semiconductor Memory devices, Read-Only memories (ROMs), flash memories, erasable ROMs (eroms), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (10)
1. A method for determining antenna weight is characterized in that the method comprises the following steps:
acquiring 4G MDT data and 5G Measurement Reports (MR) of a plurality of User Equipment (UE) accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell, wherein the 4G MDT data comprises UE position information, the 5G MR comprises synchronous signal reference signal received power (SSB-RSRP), and the antenna file comprises a plurality of antenna weights;
dividing the coverage area of the target 5G cell into a plurality of sub-coverage areas, and respectively allocating the plurality of UEs to first sub-coverage areas corresponding to the UE position information of the UEs in the plurality of sub-coverage areas;
determining a first coverage area to be optimized in the coverage area according to the SSB-RSRP of the UE in the first sub-coverage areas, determining a plurality of first antenna weights corresponding to the first coverage area to be optimized from the plurality of antenna weights, and generating a target antenna weight set comprising the plurality of first antenna weights;
and calculating the antenna gain fraction of each antenna weight in the target antenna weight set, and determining the antenna weight of which the antenna gain fraction meets a preset gain condition in the target antenna weight set as the target antenna weight.
2. The method of claim 1, wherein the determining a first area of coverage to be optimized based on SSB-RSRP of UEs in the first plurality of coverage sub-areas comprises:
determining a first sub-coverage area, in the plurality of first sub-coverage areas, of which the SSB-RSRP of the UE meets a preset weak coverage condition as a weak coverage area;
and clustering and shaping the weak coverage area according to a clustering algorithm and a shaping algorithm to obtain the first coverage area to be optimized.
3. The method of claim 2, wherein the clustering algorithm comprises a DBSCAN algorithm and the shaping algorithm comprises an Alpha Shape algorithm.
4. The method of claim 1, wherein the calculating the antenna gain fraction for each antenna weight in the set of target antenna weights comprises:
calculating SSB-RSRP of the UE in each sub coverage area corresponding to each antenna weight in the target antenna weight set;
and calculating the antenna gain fraction of each antenna weight in the target antenna weight set according to the SSB-RSRP of the UE in each sub-coverage area corresponding to each antenna weight in the target antenna weight set.
5. The method according to claim 1, wherein the determining the antenna weight values with the antenna gain fractions satisfying a preset gain condition in the target antenna weight value set as target antenna weight values comprises:
and determining the antenna weight with the maximum antenna gain fraction in the target antenna weight set as the target antenna weight.
6. The method according to any of claims 1-5, wherein after determining the antenna weights in the set of target antenna weights for which the antenna gain fractions meet a predetermined gain condition as target antenna weights, the method further comprises:
and gradually adjusting the antenna weight value currently configured by the target antenna to the target antenna weight value according to a preset stepping algorithm.
7. The method of claim 1, further comprising:
acquiring a three-dimensional electronic map of the coverage area and 5G drive test data of drive test equipment accessed to the target 5G cell, wherein the 5G drive test data comprises sampling point position information and sampling point SSB-RSRP;
extracting feature information of buildings in the coverage area from the three-dimensional electronic map, and determining a plurality of second antenna weights corresponding to the feature information from the plurality of antenna weights;
determining a second coverage area to be optimized in the coverage area according to the sampling point position information and the sampling point SSB-RSRP, and determining a plurality of third antenna weights corresponding to the second coverage area to be optimized from the plurality of antenna weights;
the generating a target antenna weight set including the plurality of first antenna weights comprises:
and obtaining the target antenna weight set by taking a union set of the plurality of first antenna weights, the plurality of second antenna weights, the plurality of third antenna weights and the antenna weight currently configured by the target antenna.
8. An antenna weight determination device, the device comprising:
an obtaining module, configured to obtain 4G MDT data and 5G MR of multiple UEs accessing a target 5G cell, and an antenna file of a target antenna corresponding to the target 5G cell, where the 4G MDT data includes UE location information, the 5G MR includes SSB-RSRP, and the antenna file includes multiple antenna weights;
a dividing module, configured to divide a coverage area of the target 5G cell into a plurality of sub-coverage areas, and allocate the plurality of UEs to first sub-coverage areas corresponding to UE location information of the UEs in the plurality of sub-coverage areas, respectively;
a determining module, configured to determine a first coverage area to be optimized in the coverage area according to SSB-RSRP of the UE in the multiple first sub-coverage areas, determine multiple first antenna weights corresponding to the first coverage area to be optimized from the multiple antenna weights, and generate a target antenna weight set including the multiple first antenna weights;
and the calculation module is used for calculating the antenna gain fraction of each antenna weight in the target antenna weight set and determining the antenna weight of which the antenna gain fraction in the target antenna weight set meets a preset gain condition as the target antenna weight.
9. An antenna weight determination device, characterized in that the device comprises: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the antenna weight determination method of any of claims 1-7.
10. A computer-readable storage medium, having computer program instructions stored thereon, which, when executed by a processor, implement the antenna weight determination method according to any one of claims 1 to 7.
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| CN113656956B (en) * | 2021-08-11 | 2023-09-08 | 卓望信息技术（北京）有限公司 | 5G antenna weight optimization method, equipment and medium based on 4G MDT |
| CN114071352A (en) * | 2021-11-02 | 2022-02-18 | 中国联合网络通信集团有限公司 | Method and device for determining antenna weight model, storage medium and equipment |
| CN114071352B (en) * | 2021-11-02 | 2023-05-02 | 中国联合网络通信集团有限公司 | Method, device, storage medium and equipment for determining antenna weight model |
| WO2023083088A1 (en) * | 2021-11-11 | 2023-05-19 | ***通信集团设计院有限公司 | 5g weight adaptive optimization method and apparatus, computing device, and computer storage medium |
| WO2023233069A1 (en) * | 2022-05-30 | 2023-12-07 | Elisa Oyj | Method and system for providing measurement-based new radio coverage map |
| WO2024061001A1 (en) * | 2022-09-19 | 2024-03-28 | 中兴通讯股份有限公司 | Antenna parameter determination method and apparatuses |
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