CN115085832A - Weight optimization method and device, communication equipment and computer readable storage medium - Google Patents
Weight optimization method and device, communication equipment and computer readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a weight optimization method, a device, communication equipment and a computer readable storage medium, wherein the weight optimization method comprises the following steps: acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams from a pre-configured weight database, and acquiring drive test quality data of the second sub-beams obtained by acquiring user data; for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data; and determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, wherein the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams. The scheme of configuring by using the first weight parameter can solve the complex actual environment signal quality requirement and has the advantages of high calculation efficiency and low cost.
Description
Technical Field
The embodiments of the present invention relate to, but not limited to, the field of communications, and in particular, to a weight optimization method, apparatus, communications device, and computer-readable storage medium.
Background
In the current 5G network optimization construction, the configuration of wireless system parameters is an important factor influencing the system performance and system indexes. The configuration scheme of Synchronization Signal and PBCH Block (SSB) in a new air interface (NR) system includes configuration of three dimensions of time domain, frequency domain and beam, which results in more complex weight combination of Massive multiple Input multiple Output (Massive MIMO), and the scale of adjusting weight combination of sub-beams can reach tens of thousands. The traditional method has difficulty in coping with the difficulty in optimizing the 5G wireless network.
An operator is just at the initial stage of 5G network establishment, a default beam weight configuration is adopted for drive test pull network, network coverage is usually monitored based on general data analysis software or tools, when network coverage problems (such as weak coverage, over coverage and the like) are found, a targeted beam configuration adjustment scheme is formulated for a cell according to the experience of optimization workers, the problems can be solved only by repeatedly adjusting and testing because the beam configuration adjustment scheme is formulated according to the experience of the workers, and the mutual relation between adjacent cells is easily ignored, so that new problems are frequently generated while the found problems are solved, and the efficiency of formulating the beam configuration adjustment scheme is low and the cost is high.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiments of the present invention mainly aim to provide a weight optimization method, device, communication device, and computer-readable storage medium, which can meet the signal quality requirement of a complex actual environment and have high calculation efficiency and low cost.
In a first aspect, an embodiment of the present invention provides a weight optimization method, including:
acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a pre-configured weight database;
acquiring drive test quality data obtained by acquiring user data, wherein the drive test quality data corresponds to second beam parameters of a plurality of second sub-beams;
for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data;
determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, where the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams.
In a second aspect, an embodiment of the present invention further provides a weight optimization apparatus, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program, when executed by the processor, implements the weight optimization method in the foregoing first aspect.
In a third aspect, an embodiment of the present invention further provides a communication device, including the weight value optimization apparatus in the second aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where an information processing program is stored on the computer-readable storage medium, and when the information processing program is executed by a processor, the information processing program implements the weight optimization method in the foregoing first aspect.
The embodiment of the invention comprises the following steps: acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a pre-configured weight database; acquiring drive test quality data obtained by acquiring user data, wherein the drive test quality data corresponds to second beam parameters of a plurality of second sub-beams; for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data; and determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, wherein the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams. The method comprises the steps of obtaining signal quality data of a first sub-beam to be predicted based on drive test quality data of a second sub-beam, determining a first weight parameter of a cell according to the signal quality data and the drive test quality data, and solving the complex actual environment signal quality requirement by using a scheme of configuring by using the first weight parameter, and has the advantages of high calculation efficiency and low cost.
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FIG. 1 is a flowchart of a weight optimization method according to an embodiment of the present invention;
fig. 2 is a flowchart of determining signal quality data in a weight optimization method according to an embodiment of the present invention;
fig. 3 is a flowchart of determining a first weight parameter in a weight optimization method according to an embodiment of the present invention;
fig. 4 is a flowchart of optimizing cell weight parameters in a weight optimization method according to an embodiment of the present invention;
fig. 5 is a flowchart of determining a second weight parameter of a cell in a weight optimization method according to an embodiment of the present invention;
fig. 6 is a flowchart of determining a second weight parameter of a cell in a weight optimization method according to an embodiment of the present invention;
FIG. 7 is a flowchart of a weight optimization method according to another embodiment of the present invention;
FIG. 8 is a diagram of a weight optimization apparatus according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a communication device provided by an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The invention provides a weight optimization method, a device, communication equipment and a computer readable storage medium, wherein the weight optimization method comprises the following steps: acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a pre-configured weight database; acquiring drive test quality data obtained by acquiring user data, wherein the drive test quality data corresponds to second beam parameters of a plurality of second sub-beams; for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data; and determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, wherein the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams. The method comprises the steps of obtaining signal quality data of a first sub-beam to be predicted based on drive test quality data of a second sub-beam, determining a first weight parameter of a cell according to the signal quality data and the drive test quality data, and solving the complex actual environment signal quality requirement by using a scheme of configuring by using the first weight parameter, and has the advantages of high calculation efficiency and low cost.
The embodiments of the present invention will be further explained with reference to the drawings.
As shown in fig. 1, fig. 1 is a flowchart of a weight optimization method according to an embodiment of the present invention. In the example of fig. 1, in an embodiment, the weight optimization method includes, but is not limited to, step S110 and step S140.
Step S110, acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a pre-configured weight database;
step S120, acquiring drive test quality data obtained by acquiring user data, wherein the drive test quality data corresponds to second beam parameters of a plurality of second sub-beams;
step S130, for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data;
step S140, determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, where the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams.
In an embodiment, a first beam parameter of a plurality of first sub-beams to be predicted and a second beam parameter of a plurality of second sub-beams to be predicted may be obtained in a preconfigured weight database, drive test quality data obtained by collecting user data may be obtained, the drive test quality data corresponds to the second beam parameter of the plurality of second sub-beams, then signal quality data of the first sub-beams may be obtained according to the first beam parameter, the second beam parameter, and the drive test quality data, each first sub-beam is traversed to obtain signal quality data of all first sub-beams, and then the first weight parameter of the cell is determined according to the signal quality data of all first sub-beams and the drive test quality data of all second sub-beams. The method comprises the steps of obtaining signal quality data of a first sub-beam to be predicted based on drive test quality data of a second sub-beam, determining first weight parameters of all cells according to the signal quality data and the drive test quality data, solving the complex actual environment signal quality requirement by using the scheme of configuring by using the first weight parameters, and having the advantages of high calculation efficiency and low cost.
It should be noted that the preconfigured weight database may be a preset database in a first antenna configuration scenario, or may be a database to be optimized in an antenna using process.
It should be noted that the first beam parameter may include the first gain data, and may also include the first beam combination information and the first gain data, which is not specifically limited in this embodiment.
It should be noted that the drive test quality data may include a first Reference Signal Receiving Power (RSRP) value of the target cell corresponding to the first beam, and may include the first RSRP value, a second RSRP value of a neighboring cell of the target cell covered by the first beam, and an interference Signal value of the first beam, which is not specifically limited in this embodiment.
It should be noted that the drive test quality data may be an RSRP value calculated by the drive test quality data, the first beam parameter, and the second beam parameter, which is not limited in this embodiment.
It should be noted that the method for acquiring the user data may be acquisition by a professional acquisition device, and may be acquisition by user measurement data of a base station mobile phone, which is not specifically limited in this embodiment.
It should be noted that the weight parameter of the cell may be a weight parameter corresponding to one cell covered by all the first sub-beams and all the second sub-beams, may also be a weight parameter corresponding to all cells covered by all the first sub-beams and all the second sub-beams, and may also be a weight parameter corresponding to multiple cells covered by all the first sub-beams and all the second sub-beams, which is not specifically limited in this embodiment.
Referring to fig. 2, in an embodiment, when the number of the second sub-beams is two or more, the first beam parameter includes first beam combination information and first gain data, and the second beam parameter includes second beam combination information and second gain data, step S130 includes, but is not limited to, step S210, step S220, step S230, and step S240:
step S210, obtaining distance parameters according to first beam combination information and all second beam combination information corresponding to the first sub-beams, wherein the distance parameters comprise distance values of all second beams relative to the first beams;
it can be understood that the distance parameter can be calculated by using a euclidean distance formula, where if the horizontal direction angle of the sub-beams is α, the vertical direction angle is β, the first sub-beam is k, and the second sub-beam is i, the euclidean distance between the first sub-beam and the second sub-beam is calculated by using the euclidean distance formula
The closer the distance between the first sub-beam and the second sub-beam is, the greater the similarity between the two sub-beams is, the coverage areas of the two sub-beams are approximately consistent, and the estimation of the signal quality data of the first sub-beam by using the drive test quality data of the second sub-beam is more accurate and reliable.
It should be noted that the calculation method of the distance parameter may be an euclidean distance calculation method, may be a manhattan distance calculation method, and may be a chebyshev distance calculation method, and the calculation method of the distance parameter is not particularly limited in this embodiment, and other formulas capable of calculating a distance value representing the similarity between the second beam and the first beam are also within the scope of this embodiment.
It should be noted that the first beam combination information may include a sub-beam horizontal angle and a sub-beam vertical angle, and may also include a sub-beam horizontal angle, a sub-beam vertical angle, a sub-beam horizontal width, and a sub-beam vertical width, which is not specifically limited in this embodiment.
Step S220, determining a plurality of second sub-beams as reference sub-beams according to the distance parameters;
it should be noted that the number of the reference sub-beams may be one, two, or three, and this embodiment does not specifically limit the number.
Step S230, acquiring second gain data and drive test quality data corresponding to the reference sub-beams;
it will be appreciated that for each reference beamlet its corresponding second gain data and drive test quality data may be obtained, which are used for subsequent calculation of the signal quality data for the first beamlet.
Step S240, obtaining signal quality data according to the first gain data, the second gain data and the drive test quality data.
It can be understood that the signal quality data of the first sub-beam can be obtained according to the first gain data of the first sub-beam, and the second gain data and the drive test quality data of the second sub-beam, and when the signal quality data is used for determining the first weight parameter, the scheme of configuring by using the first weight parameter can solve the complex actual environment signal quality requirement, and has the advantages of high calculation efficiency and low cost.
In an embodiment, the distance parameter may be obtained according to first beam combination information and all second beam combination information corresponding to the first sub-beam, and the distance parameter may include distance values of all second beams relative to the first beam, then, the plurality of second sub-beams are determined as reference sub-beams according to the distance values of all second beams relative to the first beam, second gain data and drive test quality data corresponding to the plurality of reference sub-beams are obtained, and the signal quality data may be obtained according to the first gain data, the second gain data and the drive test quality data. The signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the scheme of configuring by using the first weight parameter can solve the complex actual environment signal quality requirement, and has the advantages of high calculation efficiency and low cost.
In an embodiment, the distance parameter may be obtained according to first beam combination information and all second beam combination information corresponding to the first sub-beam, the distance parameter may include distance values of all second beams relative to the first beam, then, the plurality of second sub-beams are determined as reference sub-beams according to the distance values of all second beams relative to the first beam, the distance parameters of the plurality of reference sub-beams are all smaller than the distance parameters of other second sub-beams, second gain data and drive test quality data corresponding to the plurality of reference sub-beams are obtained, and signal quality data may be obtained according to the first gain data, the second gain data and the drive test quality data. The distance values of all the second beams relative to the first beam are calculated through the Euclidean distance formula, then a plurality of second sub-beams with smaller distance parameters are confirmed to be reference sub-beams, the smaller the distance parameter of the reference sub-beams is, the higher the similarity between the reference sub-beams and the first beam is, the more accurate the determined signal quality data is obtained based on the second gain data and the road test quality data of the reference sub-beams, and the signal quality data is obtained based on the road test quality data of the second sub-beams, when the signal quality data is used for determining the first weight parameters, the complex actual environment signal quality requirements can be solved by using the scheme of configuring the first weight parameters, and the advantages of high calculation efficiency and low cost are achieved.
In an embodiment, when the number of the reference sub-beams is 2, the first sub-beam to be predicted is k, the two reference beams with smaller distance parameters from the first sub-beam are i and j, respectively, and the drive test quality data obtained by collecting the user data by the two reference beams are RSRP i 、RSRP j The second gain data corresponding to the two reference beams are AntGain [ i ] respectively]、AntGain[j]The distance parameters of the two reference beams are respectively rho k,i 、ρ k,j The position of the user relative to the cell is (h, v), and the first gain data of the first sub-beam is AntGain [ k ]]Then calculating the signal quality data RSRP of the first sub-beam k The calculation method comprises the following steps:
RSRP k,i [h][v]＝RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j [h][v]＝RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
all the first sub-beams can be traversed according to the formula to obtain the signal quality data of all the first sub-beams, and the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams can be used in the subsequent step of determining the first weight parameters of the cell. Because the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the scheme of configuring by using the first weight parameter can solve the complex actual environment signal quality requirement, and has the advantages of high calculation efficiency and low cost.
Referring to fig. 3, in an embodiment, the step S140 includes, but is not limited to, a step S310 and a step S320.
Step S310, cell information of a plurality of cells is obtained, wherein each cell information comprises a quantity value of a second sub-beam covering the cell, signal quality data corresponding to a first sub-beam and a first weight parameter;
it can be understood that, when it is required to optimize the network coverage of several cells, cell information of the several cells may be obtained, where the cell information of each cell may include a quantity value of the second sub-beam of the covered cell, and signal quality data and a first weight parameter corresponding to the first sub-beam of the covered cell.
It should be noted that the number of cells may be one, two, or three, and this embodiment does not specifically limit the number.
Step S320, determining a second weight parameter of the cell according to the quantity value, the plurality of signal quality data and the plurality of first weight parameters.
It can be understood that the second weight parameter of the cell is determined according to the quantity value of the second sub-beam covering the cell, and the signal quality data and the first weight parameter corresponding to the plurality of first sub-beams covering the cell, and since the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the complex actual environment signal quality requirement can be solved by using the scheme of configuring the second weight parameter of the cell by using the first weight parameter, and the method has the advantages of high calculation efficiency and low cost.
Referring to fig. 4, in an embodiment, step S320 includes, but is not limited to, step S410, step S420, and step S430.
Step S410, sequencing all cells according to the quantity values to obtain cell sequences;
it can be understood that all cells to be configured with the second weight parameter may be sorted according to the magnitude of the number value of the second sub-beam covering the cell, and a cell sequence may be obtained.
It should be noted that the sorting may be performed in a descending order or an ascending order, and this embodiment does not specifically limit the sorting.
Step S420, determining a target cell according to the cell sequence;
it can be understood that the greater the number of samples of the second sub-beam covering a cell, the higher the requirement of the cell corresponding to the number of samples on the signal coverage quality, so that the target cell may be determined according to the situation of the cell sequence, and the target cell may preferentially configure the second weight parameter.
Step S430, determining a second weight parameter according to a plurality of corresponding signal quality data and a plurality of first weight parameters in the target cell.
It can be understood that the second weight parameter according with the signal quality condition of the target cell is obtained according to a plurality of corresponding signal quality data and a plurality of first weight parameters in the target cell as a judgment basis.
In an embodiment, all cells to be configured with the second weight parameter may be sorted according to the number of samples of the second sub-beam covering the cell, and a cell sequence may be obtained, where as the number of samples of the second sub-beam covering the cell is larger, the requirement of the cell corresponding to the number of samples for the signal coverage quality is higher, so that the target cell may be determined according to the situation of the cell sequence, the target cell may preferentially configure the second weight parameter, and then the second weight parameter meeting the signal quality situation of the target cell may be obtained according to a plurality of corresponding signal quality data and a plurality of first weight parameters in the target cell as a determination basis. Because the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the scheme of configuring the second weight parameter of the cell by using the first weight parameter can solve the complex actual environment signal quality requirement, and has the advantages of high calculation efficiency and low cost.
Referring to FIG. 5, in one embodiment, step S430 includes, but is not limited to, step S510.
Step S510 determines the first weight parameter corresponding to the maximum signal quality data in the target cell as the second weight parameter.
In an embodiment, the target cell may determine signal quality data corresponding to a plurality of first weight parameters, and then determine a first weight parameter corresponding to the largest signal quality data in the target cell as a second weight parameter.
Referring to fig. 6, in an embodiment, when the signal quality data includes a first reference signal received power RSRP value of the target cell corresponding to the first beam, a second RSRP value of a neighboring cell of the target cell covered by the first beam, and an interference signal value of the first beam, the step S430 includes, but is not limited to, the steps S610 and S620.
Step S610, obtaining an SINR value according to the first RSRP value, the second RSRP value and the interference signal value;
step S610, determining the first weight parameter corresponding to the maximum SINR value in the target cell as the second weight parameter.
In an embodiment, the SINR value may be obtained by calculating according to the first RSRP value, the second RSRP value and the interference signal value in the signal quality data, and then the first weight parameter corresponding to the maximum SINR value is used as the second weight parameter of the target cell. Because the SINR value is calculated according to the signal quality data, and the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the scheme of configuring the second weight parameter of the cell by using the first weight parameter can solve the complex actual environment signal quality requirement, and has the advantages of high calculation efficiency and low cost.
Referring to fig. 7, fig. 7 is a flowchart of a weight optimization method according to another embodiment of the present invention. In the example of fig. 7, in an embodiment, the weight optimization method includes, but is not limited to, step S701 and step S711.
Step S701, preset weight parameter configuration is carried out on a cell to be optimized;
it is understood that the preset weight parameters include one or more sets of weight parameters, and the present embodiment does not limit the preset weight parameters specifically.
Step S702, collecting user data of a cell to be optimized according to preset weight parameters;
it can be understood that the collected user data represents the beam coverage characteristics of different horizontal direction angles, different electronic downtilts and different beam wave widths of each cell, and directly reflects the actual environment of the cell; the user data may include drive test quality data, Physical Cell ID (PCI) information, SSB index information, Secondary Synchronization Signal (SSS) Signal-to-noise ratio, SSS power, SSS noise, PBCH (Physical Broadcast Channel) Signal-to-noise ratio, PBCH power, PBCH noise, user longitude and latitude, and height, which are not specifically limited in this embodiment.
It should be noted that the drive test quality data may include a first RSRP value, a second RSRP value of a neighboring cell of the target cell covered by the first beam, and an interference signal value of the first beam, which is not specifically limited in this embodiment.
Step S703, preprocessing the user data to obtain the drive test quality data;
it can be understood that the preprocessing of the user data mainly includes user data cleaning and user data smoothing to obtain drive test quality data, and the drive test quality data is used for calculating the signal quality data of the first beam to be predicted.
The user data cleaning mainly comprises the steps of standardizing a user data format, cleaning abnormal user data, cleaning repeated user data and reserving required user data. The user data smoothing process is mainly used for processing the user data in a smooth aggregation mode, a data normalization mode and the like, and reducing the user data on the basis of keeping the characteristics of the original user data.
It should be noted that the data smoothing processing manner may include spatial dimension rasterization, may include time dimension rasterization, may include data sampling, and may also include median filtering, which is not specifically limited by this embodiment.
Step S704, determining a beam weight gain database to be constructed, wherein the beam weight gain database comprises a beam combination and beam parameters corresponding to the beam combination, and the beam combination comprises a plurality of first sub-beams to be predicted and second sub-beams for acquiring user data;
it can be understood that the components of the beam weight gain database to be constructed are selected according to the scene, and the beam combination and the beam parameters corresponding to the beam combination in the beam weight gain database are mainly determined. The beam weight database can be composed of a plurality of sub-beam gain tables, each sub-beam gain table is mainly composed of beam combinations, wherein the beam combinations can be generated according to sub-beam horizontal angles, sub-beam vertical angles, sub-beam horizontal widths and sub-beam vertical widths, and the beam combinations comprise a plurality of first sub-beams to be predicted and second sub-beams of acquired user data.
It should be noted that the beam parameters may include a first beam parameter of the first sub-beam and a second beam parameter of the second sub-beam. The first beam parameters may include first beam combination information and first gain data, and the second beam parameters may include second beam combination information and second gain data.
Step S705, acquiring the relative position information of the user;
it can be understood that the cell may be used as a reference point to calculate the relative position of the user, the relative position of the user may be calculated by combining longitude and latitude position information of the user and working parameter information of the cell, and the user relative position information is mainly used to determine the first gain data of the first sub-beam and the second gain data of the second sub-beam.
Step S706, obtaining a distance parameter according to the first beam parameter and the second beam parameter, and determining a plurality of second sub-beams closest to the first sub-beam to be predicted as reference sub-beams according to the distance parameter.
It is understood that the distance parameter may be calculated according to the first beam parameter (sub-beam horizontal angle, sub-beam vertical angle) and the second beam parameter (sub-beam horizontal angle, sub-beam vertical angle), and a number of second sub-beams closest to the first sub-beam to be predicted may be determined as reference sub-beams according to the distance parameter.
It should be noted that the number of the reference sub-beams may be 1 or more, and this embodiment does not limit this.
In an embodiment, taking the euclidean distance calculation method as an example, assuming that the beam horizontal direction angle is α, the vertical direction angle is β, and the euclidean distance calculation formula with the first sub-beam being k and the second sub-beam being i is
The closer the distance value of the calculation result is, the greater the similarity between the first sub-beam and the second sub-beam is, and the more accurate and reliable the signal quality data of the first sub-beam is predicted by using the second sub-beam drive test quality data.
In step S707, second gain data and drive test quality data corresponding to the reference sub-beam are acquired.
It will be appreciated that second gain data and drive test quality data corresponding to the reference beamlets may be obtained based on the determined number of reference beamlets.
Step S708, obtaining signal quality data according to the first gain data, the second gain data, and the drive test quality data.
It can be understood that, assuming that the number of reference sub-beams is 2, the first sub-beam is k, and the first gain data is AntGain [ k ]]The two reference sub-beams closest to the first sub-beam are i and j respectively, and the second path of measurement quality data corresponding to the two reference sub-beams is RSRP i 、RSRP j The second gain data corresponding to the two reference sub-beams are AntGain [ i ] respectively]、AntGain[j]The distance parameters corresponding to the two reference sub-beams are respectively rho k,i 、ρ k,j The relative position information of the user is (h, v), and the signal quality data of the first sub-beam is RSRP k The signal quality data of the first sub-beam may beCalculated according to the following formula:
RSRP k,i [h][v]＝RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j [h][v]＝RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
in step S709, all the first sub-beams are traversed by steps S707 and S708 to obtain all the signal quality data, and according to all the signal quality data and all the drive test quality data, the first weight parameters of all the cells can be determined, and the beam weight gain database is updated.
It can be understood that the weight gain database reflects the coverage characteristics of the base station in the actual environment, and the beam weight gain database can be used to predict the signal measurement data after the first beam is dynamically adjusted, so as to provide a data base for the recommendation of the broadcast weight parameters. Wherein the beam dynamic adjustment comprises adjustment of a sub-beam horizontal angle, a sub-beam vertical angle, a sub-beam horizontal width, and a sub-beam vertical width.
Step S710, the optimal weight search is carried out on the cell according to the weight gain database, and a second weight parameter of the cell is determined.
It can be understood that a plurality of candidate weight parameters can be determined according to the updated weight gain database, then the cell is subjected to optimal weight search according to the candidate weight parameters, a second weight parameter of the cell is determined, and the cell is configured by using the second weight parameter.
It should be noted that, according to different requirements, the present step may adopt different cost functions, different candidate weight parameters, and different weight search methods, and be applied to different beam configuration scenarios.
It should be noted that, a global cost function may be used, and a local cost function may also be used to confirm the second weight parameter of the cell, so as to implement optimization of a whole area or a local area of the wireless coverage of the cell, which is not specifically limited in this embodiment.
It should be noted that, the candidate weight parameter may be configured according to an actual situation, and the candidate weight parameter is suitable for a beam configuration scenario in which a single beam, a multiple beam, and a single multiple beam coexist, which is not specifically limited in this embodiment.
It should be noted that the optimal weight search may use a deep learning manner, an enhanced learning manner, an ant colony algorithm, or a monte carlo algorithm, which is not specifically limited in this embodiment.
For the determination of the second weight parameter of the cell, the SINR value of the serving cell of the user may be used for determination, and assuming that the second beam of the serving cell is i, the SINR value may be calculated as follows:
wherein, RSRP main,SSBi For the received power, RSRP, of the cell beam i to be optimized others,SSBi For the received power, noise, of the beam i of the neighbourhood of the cell to be optimized SSBi For white noise power, linear value calculation should be used for power value calculation.
And counting the mathematical characteristics of the SINR value of the service cell, constructing a cost function, realizing purposeful optimization of the second weight parameter, and outputting the second weight parameter of the cell for configuration and issuing.
The cost function may also determine the second weight parameter according to the overall highest RSRP value of the cell, may also determine the second weight parameter according to the interference lowest value of the cell, and may also determine the second weight parameter according to the evaluation result of the integrated result of multiple cost functions, which is not specifically limited in this embodiment.
The method for determining the second weight parameter with respect to the interference minimum value is not limited to the method for determining the optimal SINR value in the embodiment, and the embodiment is not limited to the only method.
Step S711 monitors the drive test quality data of the cell configured with the second weight parameter, if the drive test quality data is greater than the signal quality threshold, the second weight parameter is not changed, and if the drive test quality data is less than the signal quality threshold, step S703 is executed.
It can be understood that based on N sets of weight gain databases, and using weight search, an optimal second weight parameter is recommended and configured to the cell, and drive test data under the second weight parameter is collected. If the drive test quality data is larger than the signal quality threshold, keeping the second weight parameter unchanged; if the drive test quality data is smaller than the signal quality threshold, the drive test quality data of the second weight parameter is collected this time, and step S703 is executed in a fallback manner. And a new weight gain database is built by the newly acquired drive test quality data and the historical data together, an optimal second weight parameter is searched, iteration is carried out until the requirement is met, and self-adaptive weight optimization can be realized.
It should be noted that the signal quality threshold may be set according to actual needs, and this embodiment does not specifically limit the signal quality threshold.
In the first embodiment of the weight optimization method based on fig. 7, the first embodiment specifically describes the above embodiments with steps and data of actual cell optimization. The specific contents are as follows:
the first embodiment: the number of the beams in the preconfigured weight database is configured to be 8, and the SSB frequency position is 2.52495 GHz. And configuring two groups of preset weight parameters for the cell to be optimized, wherein the two groups of preset weight parameters comprise data reflecting horizontal coverage and vertical coverage directions, such as a sub-beam horizontal angle, a sub-beam vertical angle, a sub-beam horizontal width, a sub-beam vertical width and the like. Table 1 below shows an example of configuring preset weight parameters for a cell with a single-layer beam and a four-layer beam, and table 2 shows user data collected based on two sets of preset weight parameters.
| Pre-configuration | Single layer beam | Four-layer beam |
| Horizontal angle of sub-beam | [-49；-32；-16；-8；8；16；32；49] | [-28；28；-28；28；-28；28；-28；28] |
| Sub-beam vertical angle | [3；3；3；3；3；3；3；3] | [-9；-9；-3；-3；3；3；9；9] |
| Horizontal width of sub-beam | [16；10；10；10；10；10；10；16] | [65；65；65；65；65；65；65；65] |
| Sub-beam vertical width | [6；6；6；6；6；6；6；6] | [6；6；6；6；6；6；6；6] |
TABLE 1
| Time | PCI | SSBIdx | SSS Sinr | SSS RSRP | SSS Pn | PBCH Sinr | PBCH RSRP | PBCH Pn | UE Lot | UE Lat | UE Alt |
| 00:00.4 | 201 | 3 | 17.75 | -99.25 | -117 | 9 | -95 | -104 | 121.61 | 31.204 | 1.5 |
| 00:00.4 | 201 | 6 | 4.25 | -112.75 | -117 | 2.75 | -106.5 | -109.25 | 121.61 | 31.204 | 1.5 |
| 00:00.4 | 307 | 0 | 14.38 | -102.75 | -117.13 | 2.5 | -98.63 | -101.13 | 121.61 | 31.204 | 1.5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 46:13.4 | 383 | 0 | 14.38 | -102.75 | -117.13 | 2.5 | -98.63 | -101.13 | 121.60 | 31.207 | 1.5 |
TABLE 2
The collected user data is reduced, and with spatial degree rasterization as an example, the data preprocessing steps may be as follows:
setting a longitude and latitude grid, wherein the longitude and latitude grid is stepped by 0.0001 degree and 3m in height according to 116.46 degrees of longitude and 39.92 degrees of latitude, and the grid size in the x-dimension direction and the x-height direction in the longitude direction is about 8.5m x 11.1.1 11.1m x 3 m;
the cell to be optimized is divided into grids, and the user data (including drive test quality data) collected by the same cell and the same sub-beam in the grids are subjected to mean value processing.
After the user data is preprocessed, a weight gain database based on different sub-beam horizontal angles, different sub-beam vertical angles, different sub-beam horizontal widths and different sub-beam vertical widths of a cell can be constructed according to the preprocessed user data, and the specific steps can be as follows:
the beam weight gain database to be constructed is determined, the preset weight parameter configuration of the embodiment is 8 beam configuration, and the use experience of the road surface and the high-rise user needs to be guaranteed. Therefore, the relevant dimensions of the beam weight gain database to be constructed in this embodiment are shown in table 3, and total 22572 sub-beams. Different sub-beams can be combined into each first weight parameter configuration, and a beam weight gain database can be obtained according to the first weight parameters. See Table 3
TABLE 3
Then, relative position information of the user is obtained, namely the relative position of the user is calculated by using the user data, information of longitude, latitude, dimension and height of the cell, the normal direction of the cell and a mechanical downward inclination angle, and taking the cell as a reference point. The relative position of the user is mainly used for inquiring the gain data (including the first gain data and the second gain data) in the theoretical antenna gain table and calculating the weight gain table of the first sub-beam to be predicted.
And obtaining a distance parameter according to the first beam parameter and the second beam parameter, and determining a plurality of second sub-beams closest to the first sub-beam to be predicted as reference sub-beams according to the distance parameter. The method comprises the following specific steps:
if a plurality of second sub-beams with the nearest distance to the kth first sub-beam to be predicted are calculated, basic user data are provided for calculating a first weight parameter of the cell to be predicted.
In this embodiment, the euclidean distance is used to measure the beam similarity, and M may be set to 2. And constructing a characteristic vector alpha, beta by using the horizontal angle of the sub-beams and the vertical angle of the sub-beams, and calculating two second sub-beams closest to the Euclidean distance of the first sub-beam to be predicted.
Assuming that the first sub-beam is k and the two second sub-beams are i and j, respectively, the first beam parameter of the first sub-beam and the second beam parameter of the two second sub-beams closer to the first sub-beam are shown in table 4:
| wave beam source | Sub-beam identification | Horizontal angle of sub-beam | Sub-beam vertical angle | Horizontal width of sub-beam | Sub-beam vertical width |
| Beams to be predicted | k | 1 | 3 | 10 | 16 |
| Near road test beam | i | -8 | 3 | 10 | 16 |
| Near drive test beam | j | 8 | 3 | 10 | 16 |
Table 4 the two second sub-beams that are most similar to the first sub-beam k to be predicted are i, j, respectively, and their euclidean distances are as follows:
and then, acquiring second gain data and drive test quality data corresponding to the reference sub-beam, and obtaining signal quality data according to the first gain data, the second gain data and the drive test quality data. The method comprises the following specific steps:
assuming that the number of reference sub-beams is 2, the first sub-beam is k, and the first gain data is AntGain [ k ]]The two reference sub-beams closest to the first sub-beam are i and j respectively, and the second path of measurement quality data corresponding to the two reference sub-beams is RSRP i 、RSRP j The second gain data corresponding to the two reference sub-beams are AntGain [ i ] respectively]、AntGain[j]The distance parameters corresponding to the two reference sub-beams are respectively rho k,i 、ρ k,j The relative position information of the user is (h, v), and the signal quality data of the first sub-beam is RSRP k The signal quality data of the first sub-beam may be calculated according to the following formula:
RSRP k,i [h][v]＝RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j [h][v]＝RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
the signal quality data may be calculated from the first gain data, the second gain data, the distance parameter, and the drive test quality data of table 5.
TABLE 5
Then the signal quality data RSRP of the first sub-beam k
And traversing all the first sub-beams to obtain all the signal quality data, determining the first weight parameters of all the cells according to all the signal quality data and all the drive test quality data, and updating the beam weight gain database.
The collected user data (including drive test quality data) and the first weight parameter of the second sub-beam are saved, and related data and a database do not need to be updated during the first iteration.
And searching the optimal weight of the cell according to the weight gain database, and determining a second weight parameter of the cell. The method comprises the following specific steps:
and aiming at optimizing the downlink coverage of the subnet area, calculating by searching the optimal weight according to the sequence of the cells. In the sub-network area, the SINR value of the serving cell under the first weight parameter estimated by all users in the area is taken as a cost function, and the optimization goal is to make the SINR value of the cell be the maximum value.
An example of the serving cell SINR calculation is as follows. Using the correlation data of table 6, the SINR value of beam 3 covered in serving cell 201 can be calculated.
| Time | PCI | SSBIdx | RSRP(dBm) | SSS Pn(dBm) |
| 00:10.4 | 201 | 3 | -89.25 | -117 |
| 00:10.4 | 202 | 3 | -112.75 | -117 |
| 00:10.4 | 307 | 3 | -102.75 | -117 |
TABLE 6
And taking the collected user data as input drive test data, counting the times of the drive test data appearing in the cell to be optimized, sequencing the cells in a descending order, preferentially selecting an alternative weight parameter for the first sequenced cell to replace the user data of the cell, and calculating by using a cost function to obtain an SINR value. After traversing all the candidate weight parameters, the cell calculates the maximum value of the SINR value and the corresponding first weight parameter, and may use the first weight parameter as the second weight parameter of the cell, and take the second weight parameter of the cell to replace the user data of the cell, and use the user data as the input data of the next cell to be optimized, and start optimizing the next cell. And traversing all the cells to be optimized by the method to obtain the second weight parameter of each cell. After the second weight parameters of all the cells are obtained, the network manager can optimize the cells according to the second weight parameters.
And monitoring the drive test quality data of the cell after the optimization of the second weight parameter, if the drive test quality data is greater than the signal quality threshold, keeping the second weight parameter unchanged, if the drive test quality data is less than the signal quality threshold, acquiring the drive test quality data of the second weight parameter at this time, returning to the step of preprocessing the user data, and optimizing the weight of the cell.
The signal quality data in the weight optimization method is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the complex actual environment signal quality requirement can be solved by using the scheme of configuring the second weight parameter of the cell by using the first weight parameter, and the method has the advantages of high calculation efficiency and low cost.
In a second embodiment of the weight optimization method based on fig. 7, the second embodiment specifically describes the above embodiments with steps and data of actual cell optimization. The specific content is as follows:
second embodiment: and carrying out a group of preset weight parameter configuration on the cell to be optimized, and collecting user data under the preset weight parameter configuration. As shown in table 7:
| pre-configuration | Four-layer beam |
| Horizontal angle of sub-beam | [-28；28；-28；28；-28；28；-28；28] |
| Sub-beam vertical angle | [-9；-9；-3；-3；3；3；9；9] |
| Horizontal width of sub-beam | [65；65；65；65；65；65；65；65] |
| Sub-beam vertical width | [6；6；6；6；6；6；6；6] |
TABLE 7
The steps of data preprocessing are the same as those in the first embodiment, and are not described herein.
After the user data is preprocessed, a weight gain database based on different sub-beam horizontal angles, different sub-beam vertical angles, different sub-beam horizontal widths and different sub-beam vertical widths of a cell can be constructed according to the preprocessed user data, and the specific steps can be as follows:
the method and the device determine a beam weight gain database to be constructed, and in the embodiment, for the purpose of guaranteeing downlink coverage and saving energy and consumption, a 1+ X SSB coverage scheme is adopted. The beam '1' provides stable and high-quality horizontal basic coverage, namely, mainly provides road surface coverage; the number of the wave beams X, the horizontal angle of the sub-wave beams, the vertical angle of the sub-wave beams, the horizontal width of the sub-wave beams and the vertical width of the sub-wave beams are flexible and variable, and scene vertical coverage is provided according to needs, namely high-rise user coverage is mainly provided. The beam structure is designed and decoupled in the horizontal direction and the vertical direction, and the optimal unification of stability and flexibility can be realized.
The relevant dimensions of the beam weight gain database to be constructed in this embodiment are shown in table 8, and total 420 sub-beams are provided.
TABLE 8
Then, relative position information of the user is obtained, namely the relative position of the user is calculated by using the user data, longitude and latitude, dimensionality and height information of the cell, the normal direction of the cell and the mechanical downward inclination angle, and taking the cell as a reference point. The relative position of the user is mainly used for inquiring the gain data (including the first gain data and the second gain data) in the theoretical antenna gain table and calculating the weight gain table of the first sub-beam to be predicted.
And obtaining a distance parameter according to the first beam parameter and the second beam parameter, and determining a plurality of second sub-beams closest to the first sub-beam to be predicted as reference sub-beams according to the distance parameter.
And acquiring second gain data and drive test quality data corresponding to the reference sub-beam, and acquiring signal quality data according to the first gain data, the second gain data and the drive test quality data.
And traversing all the first sub-beams to obtain all the signal quality data, determining a first weight parameter of the cell according to all the signal quality data and all the drive test quality data, and updating a beam weight gain database.
The collected user data (including drive test quality data) and the first weight parameter of the second sub-beam are saved, and related data and a database do not need to be updated during the first iteration.
The steps of calculating and determining the first weight parameter are the same as those of the first embodiment, and are not repeated herein.
And searching the optimal weight of the cell according to the weight gain database, and determining a second weight parameter of the cell. The method comprises the following specific steps:
in the embodiment, the index value of the alternative SSB of the wave beam '1' is [0,1,2,3,4], and the alternative SSB is sent by staggering the SSB time domain, so that the mutual interference of the SSBs in adjacent intervals can be reduced, and the high-quality basic coverage of the road surface level can be effectively guaranteed; and the interference between the SSB of the adjacent cell and the service channel can be reduced through the automatic punching processing of the service channel.
In this embodiment, the number of beams "X" is configurable, i.e. X ∈ [0,1,2,3], the alternative SSB index value is [5,6,7], and the configuration of the horizontal angle of the sub-beam, the vertical angle of the sub-beam, the horizontal width of the sub-beam, and the vertical width of the sub-beam of the beam "X" is adaptively selected by the cell according to the measurement information of the tall-building user.
The cost function of this embodiment may use the SINR values of the serving cell under the calculated first weight parameters of all users in the subnet area, and determine the maximum SINR value corresponding to the first weight parameter as the second weight parameter of the cell.
The weight search method of this embodiment may employ a weight optimization search algorithm based on the beam sequence. Firstly, the ant colony algorithm is utilized to search the optimal configuration of the wave beam '1', so that the inter-cell interference is reduced, and the horizontal coverage of the road surface is ensured. And secondly, searching the optimal configuration of the beam X1, 2 and 3 in sequence by using an ant colony algorithm, and ensuring the perception of users of middle and high-rise buildings.
And then optimizing the cell by using the second weight parameter, monitoring the drive test quality data of the cell after the optimization of the second weight parameter is passed, if the drive test quality data is greater than a signal quality threshold, keeping the second weight parameter unchanged, if the drive test quality data is less than the signal quality threshold, acquiring the drive test quality data of the second weight parameter at this time, returning to the step of preprocessing user data, and optimizing the weight of the cell.
The signal quality data in the weight optimization method is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used for determining the first weight parameter, the complex actual environment signal quality requirement can be solved by using the scheme of configuring the second weight parameter of the cell by using the first weight parameter, and the method has the advantages of high calculation efficiency and low cost.
In addition, an embodiment of the present invention further provides a weight value optimization apparatus, and referring to fig. 8, the weight value optimization apparatus 800 includes a memory 820, a processor 810, and a computer program stored in the memory 820 and executable on the processor 810.
The processor 810 and the memory 820 may be connected by a bus or other means.
The memory 820, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory 820 may include high-speed random access memory 820, and may also include non-transitory memory 820, such as at least one piece of disk memory 820, flash memory device, or other piece of non-transitory solid state memory 820. In some embodiments, the memory 820 may optionally include memory 820 located remotely from the processor 810, and these remote memories 820 may be connected to the processor 810 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions required to implement the information processing method of the above-described embodiment are stored in the memory, and when executed by the processor, perform the weight optimization method of the above-described embodiment, for example, perform the above-described method steps S110 to S140 in fig. 1, method steps S210 to S240 in fig. 2, method steps S310 to S320 in fig. 3, method steps S410 to S430 in fig. 4, method step S510 in fig. 5, method steps S610 to S620 in fig. 6, and method steps S701 to S711 in fig. 7.
In addition, an embodiment of the present invention further provides a communication device, and referring to fig. 9, the communication device 900 includes the weight value optimizing apparatus 800 described above. The weight optimization device 800 may obtain first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a preconfigured weight database, and obtain drive test quality data obtained by collecting user data, where the drive test quality data corresponds to the second beam parameters of the plurality of second sub-beams, then may obtain signal quality data of the first sub-beams according to the first beam parameters, the second beam parameters, and the drive test quality data, traverse each first sub-beam to obtain signal quality data of all first sub-beams, and then determine the first weight parameters of the cell according to the signal quality data of all first sub-beams and the drive test quality data of all second sub-beams. The method comprises the steps of obtaining signal quality data of a first sub-beam to be predicted based on drive test quality data of a second sub-beam, and determining a first weight parameter of a cell according to the signal quality data and the drive test quality data.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, which are executed by a processor or a controller, for example, by one of the weight optimization devices in the above embodiments, and can enable the processor to execute the weight optimization method in the above embodiments, for example, execute the method steps S110 to S140 in fig. 1, the method steps S210 to S240 in fig. 2, the method steps S310 to S320 in fig. 3, the method steps S410 to S430 in fig. 4, the method steps S510 in fig. 5, the method steps S610 to S620 in fig. 6, and the method steps S701 to S711 in fig. 7, which are described above.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (11)
1. A weight optimization method comprises the following steps:
acquiring first beam parameters of a plurality of first sub-beams to be predicted and second beam parameters of a plurality of second sub-beams in a pre-configured weight database;
acquiring drive test quality data obtained by acquiring user data, wherein the drive test quality data corresponds to second beam parameters of a plurality of second sub-beams;
for each first sub-beam, obtaining signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data;
determining a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, where the first weight parameter is a weight parameter corresponding to cells covered by all the first sub-beams and all the second sub-beams.
2. The weight optimization method according to claim 1, wherein when the number of the second sub-beams is two or more, the first beam parameters include first beam combination information and first gain data, the second beam parameters include second beam combination information and second gain data, and the obtaining signal quality data according to the first beam parameters, the second beam parameters, and the drive test quality data comprises:
obtaining a distance parameter according to first beam combination information corresponding to the first sub-beam and all the second beam combination information, wherein the distance parameter comprises distance values of all the second beams relative to the first beam;
determining a plurality of second sub-beams as reference sub-beams according to the distance parameters;
acquiring the second gain data and the drive test quality data corresponding to the reference sub-beams;
and obtaining signal quality data according to the first gain data, the second gain data and the drive test quality data.
3. The weight optimization method of claim 2, wherein obtaining the distance parameter according to the first beam combination information corresponding to the first sub-beam and all the second beam combination information comprises:
and calculating by using the first beam combination information corresponding to the first sub-beam and all the second beam combination information through an Euclidean distance formula to obtain a distance parameter.
4. A weight optimization method according to claim 2 or 3, wherein the distance parameters of some of the reference sub-beams are smaller than the distance parameters of other second sub-beams.
5. The weight optimization method according to claim 1, further comprising:
acquiring cell information of a plurality of cells, wherein each cell information comprises a quantity value of the second sub-beams covering the cell, the signal quality data corresponding to the first sub-beams and the first weight parameter;
and determining a second weight parameter of the cell according to the quantity value, the plurality of signal quality data and the plurality of first weight parameters.
6. The method of claim 5, wherein the determining the second weight parameter of the cell according to the quantity value, the plurality of signal quality data and the plurality of first weight parameters comprises:
sequencing all the cells according to the quantity value to obtain a cell sequence;
determining a target cell according to the cell sequence;
and determining a second weight parameter according to a plurality of corresponding signal quality data and a plurality of first weight parameters in the target cell.
7. The method of claim 6, wherein the determining a second weight parameter according to a plurality of the first weight parameters and a plurality of the signal quality data corresponding to the target cell comprises:
and determining the first weight parameter corresponding to the maximum signal quality data in the target cell as a second weight parameter.
8. The weight optimization method according to claim 6, wherein the signal quality data comprises a first Reference Signal Received Power (RSRP) value of the target cell corresponding to the first beam, a second RSRP value of a neighboring cell of the target cell covered by the first beam, and an interference signal value of the first beam, and the determining the second weight parameter according to a corresponding number of the signal quality data and a number of the first weight parameters in the target cell comprises:
obtaining a signal to interference plus noise ratio (SINR) value according to the first RSRP value, the second RSRP value and the interference signal value;
and determining the first weight parameter corresponding to the maximum SINR value in the target cell as a second weight parameter.
9. A weight optimization device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the weight optimization method according to any one of claims 1 to 8 when executing the computer program.
10. A communication device comprising the weight optimization apparatus of claim 9.
11. A computer-readable storage medium storing computer-executable instructions for performing the weight optimization method of any one of claims 1-8.
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110289746.9A Pending CN115085832A (en) | 2021-03-16 | 2021-03-16 | Weight optimization method and device, communication equipment and computer readable storage medium |
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| CN (1) | CN115085832A (en) |
| WO (1) | WO2022193717A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024078145A1 (en) * | 2022-10-12 | 2024-04-18 | 中兴通讯股份有限公司 | Beam weight optimization method and apparatus |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US10171150B1 (en) * | 2017-06-26 | 2019-01-01 | Sprint Communications Company L.P. | Dynamic optimization of beamforming weights |
| CN110730466B (en) * | 2018-07-16 | 2023-05-26 | 中兴通讯股份有限公司 | Method and device for determining broadcast beam weight, network element and storage medium |
| CN111385039B (en) * | 2018-12-30 | 2022-04-22 | 上海华为技术有限公司 | Method and device for adjusting cell broadcast beam parameters |
| CN112350756B (en) * | 2019-08-08 | 2021-12-07 | 中国移动通信集团广东有限公司 | Method and device for optimizing weight parameters of antenna and electronic equipment |
| CN111465026B (en) * | 2020-03-26 | 2023-04-14 | 北京拓明科技有限公司 | Antenna weight parameter adjusting method |
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2021
- 2021-03-16 CN CN202110289746.9A patent/CN115085832A/en active Pending
- 2021-11-24 WO PCT/CN2021/132854 patent/WO2022193717A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024078145A1 (en) * | 2022-10-12 | 2024-04-18 | 中兴通讯股份有限公司 | Beam weight optimization method and apparatus |
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| WO2022193717A1 (en) | 2022-09-22 |
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