Antenna parameter optimization method, electronic device and storage medium

Abstract

Provided in the embodiments of the present disclosure are an antenna parameter optimization method, an electronic device and a storage medium. The antenna parameter optimization method comprises: acquiring initial values of antenna parameters of cells in a target region and a plurality of measurement reports of the target region; and on the basis of the initial values of the antenna parameters of the cells in the target region and the plurality of measurement reports of the target region, performing optimization on the antenna parameters of the cells in the target region by taking a regional user perception indicator being optimal as an objective, and determining target values of the antenna parameters of the cells in the target region, wherein the regional user perception indicator is used for characterizing the quality of experience perceived by users in the target region for a wireless communication service.

Description

Antenna parameter optimization method, electronic device and storage medium

[0001] This application claims priority to Chinese patent application No. 202311868309.8 filed on December 29, 2023, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present disclosure relates to the field of communication technology, and in particular to an antenna parameter optimization method, electronic device, and storage medium. Background Art

[0003] In the field of communications technology, optimizing antenna parameters to improve signal reception and transmission efficiency is a common technical approach. This typically involves manual optimization based on expert experience and defining antenna parameter optimization targets based on wireless metrics.

[0004] Summary of the Invention

[0005] In a first aspect, a method for optimizing antenna parameters is provided. The method includes: obtaining initial values ​​of antenna parameters for each cell in a target area and multiple measurement reports for the target area; optimizing the antenna parameters for each cell in the target area based on the initial values ​​of the antenna parameters for each cell in the target area and the multiple measurement reports for the target area, with the goal of optimizing a regional user perception indicator (RPI), and determining target values ​​for the antenna parameters for each cell in the target area. The regional user perception indicator (RPI) represents the perceived quality of experience of wireless communication services for users in the target area.

[0006] In a second aspect, an electronic device is provided, comprising: an acquisition module configured to acquire initial values ​​of antenna parameters of each cell in a target area and multiple measurement reports for the target area; and a processing module configured to optimize the antenna parameters of each cell in the target area based on the initial values ​​of the antenna parameters of each cell in the target area and the multiple measurement reports for the target area, with the goal of optimizing a regional user perception indicator (RPI), and determine target values ​​for the antenna parameters of each cell in the target area. The regional user perception indicator (RPI) represents the perceived quality of experience of wireless communication services for users in the target area.

[0007] In a third aspect, another electronic device is provided, comprising: a memory and a processor. The memory is coupled to the processor; the memory is used to store instructions executable by the processor; and when the processor executes the instructions, the antenna parameter optimization method of the first aspect is performed.

[0008] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the antenna parameter optimization method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] To more clearly illustrate the technical solutions of the present disclosure, the following briefly introduces the drawings required for use in some embodiments of the present disclosure. Obviously, the drawings described below are only drawings of some embodiments of the present disclosure, and those skilled in the art can also derive other drawings based on these drawings.

[0010] FIG1 is a diagram of a scenario architecture for antenna parameter optimization according to an embodiment of the present disclosure.

[0011] FIG2 is a schematic flow chart of a method for optimizing antenna parameters according to an embodiment of the present disclosure.

[0012] FIG3 is a schematic diagram of a process for determining a regional user perception index according to an embodiment of the present disclosure.

[0013] FIG4 is a schematic diagram of another process for determining a regional user perception index according to an embodiment of the present disclosure.

[0014] FIG5 is a schematic diagram of another process for determining a regional user perception index according to an embodiment of the present disclosure.

[0015] FIG6 is a schematic diagram of another process for determining a regional user perception index according to an embodiment of the present disclosure.

[0016] FIG7 is a schematic diagram of another process for determining a regional user perception index according to an embodiment of the present disclosure.

[0017] FIG8 is a schematic diagram of another process for determining a regional user perception index according to an embodiment of the present disclosure.

[0018] Figure 9 is a training flowchart of a cell perception twin model according to an embodiment of the present disclosure.

[0019] FIG10 is a flow chart of determining a cell-level user perception index according to an embodiment of the present disclosure.

[0020] FIG11 is a flow chart of a method for optimizing antenna parameters according to an embodiment of the present disclosure.

[0021] FIG12 is a flowchart of another antenna parameter optimization method according to an embodiment of the present disclosure.

[0022] FIG13 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

[0023] FIG14 is a schematic structural diagram of another electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present disclosure.

[0025] In the description of the present disclosure, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only used to describe an association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: only A, only B, and A and B. In addition, "at least one" means one or more, and "a plurality" means two or more. Words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not limit them to be necessarily different.

[0026] It should be noted that, in this disclosure, words such as "exemplary" or "for example" are used to describe examples, illustrations, or explanations. Any embodiment or design described in this disclosure using words such as "exemplary" or "for example" should not be interpreted as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0027] Currently, there are no methods in the communications technology field that can effectively optimize antenna parameters. Furthermore, with the rapid and large-scale deployment of fifth-generation mobile communication technology (5G), the density of high-frequency 5G base stations has increased significantly, making collaborative optimization between base stations more difficult. This makes optimization methods based on the historical experience of relevant experts difficult. Furthermore, defining antenna parameter optimization targets based on wireless indicators fails to consider user perception, resulting in ineffective optimization of antenna parameters. Therefore, how to effectively optimize antenna parameters is an urgent problem that needs to be solved.

[0028] Based on this, the present disclosure provides a method for optimizing antenna parameters. By using a regional user perception indicator that characterizes the perceived experience quality of wireless communication services by users in a target area as the optimization target for the antenna parameters of each cell in the target area, the antenna parameters can be effectively optimized according to the user's perceived experience, thereby enabling the communication service to better meet the user's needs and improve the user's communication service experience.

[0029] The antenna parameter optimization method provided by the present disclosure can be applied to the antenna parameter optimization scenario architecture shown in Figure 1. Figure 1 shows a schematic diagram of an antenna parameter optimization scenario architecture according to an embodiment of the present disclosure. As shown in Figure 1, the scenario architecture includes a target area 10.

[0030] As shown in Figure 1 , target area 10 includes an electronic device 20 and multiple cells, such as cell 100, cell 110, and cell 120. For example, a cell is defined as the coverage area of ​​a base station. Each cell has a base station. Electronic device 20 can communicate with the base stations in each cell via wireless channels.

[0031] In some embodiments, the base station in each cell can be a base station or an evolved base station (eNB or eNodeB) in long term evolution (LTE), long term evolution advanced (LTE-A), a base station device in a 5G network, or a base station in a future communication system, etc., and can include various macro base stations, micro base stations, home base stations, wireless remote devices, reconfigurable intelligent surfaces (RIS), routers, wireless fidelity (WIFI) devices and other network side devices.

[0032] In some embodiments, the electronic device 20 can obtain the initial values ​​of the antenna parameters of each cell through the base station of each cell to obtain the initial values ​​of the antenna parameters of the entire target area 10, and obtain multiple measurement reports of the target area 10 through the base station of each cell, and based on the initial values ​​of the antenna parameters of each cell in the target area 10 and the multiple measurement reports of the target area 10, with the goal of optimizing the regional-level user perception indicators, optimize the antenna parameters of each cell in the target area and determine the target values ​​of the antenna parameters of each cell in the target area.

[0033] It should be noted that, for simplicity in FIG1 , only three cells are shown in the target area 10. In practice, the target area may include a greater number of cells, and this disclosure does not limit this. It should also be noted that FIG1 is merely an exemplary framework diagram, and the number of devices included in FIG1 and the names of the devices are not limited. In addition to the devices shown in FIG1 , other devices may also be included in the scenario architecture.

[0034] The application scenarios of the embodiments of the present disclosure are not limited. The system architecture and business scenarios described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. It is known to those skilled in the art that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0035] Fig. 2 shows a flow chart of a method for optimizing antenna parameters according to an embodiment of the present disclosure, which is applied to the electronic device 20 in Fig. 1. The method for optimizing antenna parameters includes the following steps S101 and S102.

[0036] In S101 , initial values ​​of antenna parameters of each cell in a target area and multiple measurement reports of the target area are obtained.

[0037] Antenna parameters include at least one of the following: antenna power, antenna downtilt angle. Measurement reports (MRs) are periodically sent by electronic devices to base stations within a target area to provide information about received signal strength, instructions, and other wireless network parameters.

[0038] In some embodiments, in response to receiving the optimization instruction, the electronic device obtains initial values ​​of antenna parameters of each cell in the target area and multiple measurement reports of the target area.

[0039] Exemplarily, in response to receiving the optimization instruction, the electronic device establishes a connection with the base station to which each cell in the target area belongs, and transmits data with the above base station to obtain the initial values ​​of the antenna parameters of each cell in the target area and multiple measurement reports of the target area.

[0040] It should be understood that the initial values ​​of the antenna parameters may be initial values ​​selected based on experience or randomly, or values ​​obtained in a historical optimization process.

[0041] In S102, based on the initial values ​​of the antenna parameters of each cell in the target area and multiple measurement reports of the target area, the antenna parameters of each cell in the target area are optimized with the goal of optimizing the regional user perception index, and the target values ​​of the antenna parameters of each cell in the target area are determined.

[0042] The regional user perception index is used to characterize the perceived quality of experience of wireless communication services for users in the target area.

[0043] For example, when optimizing the antenna parameters of each cell in the target area, the antenna parameters of the target area are used as optimization parameters, and minimizing the regional user perception index is used as the optimization goal. Furthermore, various optimization algorithms can be used during the optimization process, and this disclosure does not limit the optimization algorithm used.

[0044] As an implementation manner, as shown in FIG3 , the regional user perception index is determined according to the following S201 and S202 .

[0045] In S201 , key performance indicators of each cell in the target area are determined based on initial values ​​of antenna parameters of each cell in the target area, multiple measurement reports of the target area, and candidate values ​​of antenna parameters of each cell in the target area during optimization.

[0046] It should be noted that the candidate values ​​for the antenna parameters of each cell in the target area during the optimization process are potential optimal solutions obtained through a specific algorithm and used for trial. During the optimization process, multiple candidate values ​​are generated based on the current solution, and the next optimization direction is determined by comparing these candidate values. Different optimization algorithms use different strategies to generate and select candidate values.

[0047] The key performance indicators include at least one of the following: a first key performance indicator, a second key performance indicator, and a third key performance indicator. The first key performance indicator is used to characterize network coverage performance, the second key performance indicator is used to characterize network capacity, and the third key performance indicator is used to characterize network interference level.

[0048] In some embodiments, the key performance indicators of each cell in the target area can be determined based on the initial values ​​of the antenna parameters of each cell in the target area, multiple measurement reports of the target area, and candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, and with reference to historical antenna parameter optimization results.

[0049] Exemplarily, after obtaining the initial values ​​of the antenna parameters of each cell in the target area and multiple measurement reports of the target area, the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process are combined with the historical antenna parameter optimization results, or based on the historical experience of relevant experts, it is determined that the A indicator of each cell in the target area best represents the network coverage performance, the B indicator best represents the network capacity, and the C indicator best represents the degree of network interference. The above three indicators A, B, and C are used as the first key performance indicator, the second key performance indicator, and the third key performance indicator in this disclosure to determine the key performance indicators of each cell in the target area.

[0050] In other embodiments, as shown in FIG4 , key performance indicators of each cell in the target area are determined based on the initial values ​​of the antenna parameters of each cell in the target area, multiple measurement reports of the target area, and candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, which can be implemented as the following S301 and S302.

[0051] In S301, multiple measurement reports are updated according to the initial values ​​of the antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process to obtain multiple updated measurement reports.

[0052] The measurement report includes the identifier of the primary serving cell and measurement information of at least one cell. The at least one cell includes a serving cell and a neighboring cell, and the primary serving cell is one of the serving cells.

[0053] Exemplarily, the measurement report also includes the location information of the user electronic device, the physical resource blocks transmitted by the physical uplink shared channel, the physical resource blocks transmitted by the physical downlink shared channel, the identity (ID) of each cell in the serving cell and the neighboring cell, and the reference signal receiving power (RSRP) of the primary serving cell and the neighboring cell.

[0054] It should be understood that before the optimization process, the multiple measurement reports of the target area obtained by the electronic device are multiple measurement reports corresponding to the initial values ​​of the antenna parameters of each cell in the target area. During the optimization process, the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process are continuously updated, so the corresponding measurement reports also need to be updated. The candidate values ​​of the antenna parameters of each cell in the target area during the optimization process correspond to multiple updated measurement reports. For example, during the optimization process, the RSRP of the primary serving cell and the neighboring cells in the measurement report will change, and the primary serving cell will also switch.

[0055] As an implementation method, multiple measurement reports are updated based on the initial values ​​of the antenna parameters of each cell in the target area, the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, and the update algorithm to obtain multiple updated measurement reports.

[0056] In some embodiments, the update algorithm includes the following formula: R′=p(R,x)=R+p′(x0,x)

[0057] Where R′ is the updated measurement report, R is the measurement report before the update, x0 is the initial value of the antenna parameter, x is the candidate value of the antenna parameter during the optimization process, and p′(x0,x) is the RSRP of the primary serving cell and the neighbor cell in the updated measurement report.

[0058] As another implementation method, as shown in Figure 5, multiple measurement reports are updated based on the initial values ​​of the antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process to obtain multiple updated measurement reports, which can be implemented as the following S401 to S403.

[0059] In S401, for each measurement report in the multiple measurement reports, the measurement information of at least one cell in the measurement report is updated according to the initial values ​​of the antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, so as to obtain updated measurement information of the at least one cell.

[0060] It should be noted that in the optimization process, each cell in the target area is used as a unit. Therefore, when updating the measurement report, the measurement information of at least one cell in the measurement report needs to be used as the update unit to improve the accuracy of the updated measurement report.

[0061] For example, the measurement information of at least one cell may include parameters such as signal strength, signal-to-noise ratio, and interference value of the serving cell and neighboring cells. During the optimization process, multiple parameters in the measurement information may also change.

[0062] In S402, based on the updated measurement information of at least one cell, the primary serving cell corresponding to the measurement report is re-determined.

[0063] It should be understood that during the optimization process, the primary serving cell corresponding to the measurement report may or may not be switched. Therefore, during the process of updating the measurement report, the primary serving cell corresponding to the measurement report needs to be re-determined.

[0064] Exemplarily, the measurement information of the primary serving cell includes the primary serving cell identifier, while the measurement information of the neighboring cell does not include the primary serving cell identifier. Therefore, the primary serving cell corresponding to the measurement report can be determined by whether the measurement information includes the primary serving cell identifier.

[0065] In another exemplary embodiment, the primary serving cell corresponding to the measurement report may also be determined by a cell determination algorithm. For example, the cell determination algorithm includes the following formula: s′=α(R′)

[0066] Wherein, s′ is the newly determined primary serving cell, α is the cell determination model, and R′ is the updated measurement information of at least one cell.

[0067] In another exemplary embodiment, the primary serving cell may be determined directly based on the RSRP of the serving cell and the neighboring cells. For example, the cell with the largest RSRP among the serving cell and the neighboring cells may be used as the primary serving cell.

[0068] As another example, the primary serving cell may be determined based on a cell interoperability parameter.

[0069] It should be noted that the primary serving cell corresponding to the measurement report may be determined by using one or more of the above-mentioned determination methods, or other determination methods may be used, and this disclosure is not limited to this.

[0070] In S403, an updated measurement report is generated based on the newly determined identifier of the primary serving cell and the updated measurement information of at least one cell.

[0071] For example, during the optimization process, the primary serving cell is switched from cell A to cell B. Therefore, the identifier of the primary serving cell corresponding to the updated measurement report is switched from the identifier of cell A to the identifier of cell B. Furthermore, the measurement information of the at least one cell included in the measurement report is also updated from the original measurement information of the at least one cell to the measurement information of the at least one cell updated during the optimization process.

[0072] It should be noted that the method for updating the measurement report also includes an update method based on a transmission model and an update method based on artificial intelligence training, which is not limited in this disclosure.

[0073] In S302 , key performance indicators of each cell in the target area are determined based on the multiple updated measurement reports.

[0074] As an implementation manner, as shown in FIG6 , determining the key performance indicators of each cell in the target area based on multiple updated measurement reports can be implemented as the following S501 and S502 .

[0075] In S501, for each cell in the target area, at least one updated measurement report with the cell as the primary serving cell is determined from a plurality of updated measurement reports.

[0076] It should be understood that each cell in the target area may be the primary serving cell of the measurement report or a non-primary serving cell in different updated measurement reports. Each cell in the target area has at least one updated measurement report with itself as the primary serving cell.

[0077] In S502, a key performance indicator of the cell is determined based on at least one updated measurement report with the cell as the primary serving cell.

[0078] Exemplarily, at least one updated measurement report of the cell as the primary serving cell includes a key performance indicator of the measurement report. The key performance indicator of the measurement report includes at least one of the following: a first key performance indicator, a second key performance indicator, and a third key performance indicator. The first key performance indicator is used to characterize network coverage performance, the second key performance indicator is used to characterize network capacity, and the third key performance indicator is used to characterize the degree of network interference.

[0079] In some embodiments, the key performance indicator of the cell may be determined based on the key performance indicator of at least one updated measurement report with the cell as the primary serving cell.

[0080] For example, take the first key performance indicator among the key performance indicators as an example. The first key performance indicator of the measurement report includes the RSRP of the primary service cell and the RSRP of the neighboring cell. Based on the first key performance indicator of at least one updated measurement report of the cell being the primary service cell, it can be determined whether the measurement report has overlapping coverage, and based on the proportion of measurement reports with overlapping coverage, the first key performance indicator among the key performance indicators of the cell is determined. The judgment condition for the existence of overlapping coverage in the measurement report is: the RSRP of the primary service cell in the first key performance indicator of the measurement report is greater than or equal to the first threshold value, and the difference between the RSRP of the primary service cell and the RSRP of the neighboring cell is less than the second threshold value and greater than or equal to the third threshold value. The measurement report that meets this judgment condition is determined to have overlapping coverage.

[0081] In other embodiments, as shown in FIG. 7 , determining the key performance indicators of a cell based on at least one updated measurement report with the cell as the primary serving cell may be implemented as the following S601 and S602 .

[0082] In S601, based on at least one updated measurement report of the cell as a primary serving cell, key performance indicators of each grid in the cell are determined.

[0083] Exemplarily, at least one updated measurement report with the cell as the main service cell is gridded so that each grid in the cell has a corresponding gridded measurement report, and based on the above gridded measurement report, the key performance indicators of the corresponding grid are determined.

[0084] The grid's key performance indicators include at least one of the following: a first key performance indicator, a second key performance indicator, and a third key performance indicator. The first key performance indicator is used to characterize network coverage performance, the second key performance indicator is used to characterize network capacity, and the third key performance indicator is used to characterize network interference level.

[0085] In S602, the key performance indicators of the cell are determined based on the key performance indicators of each grid in the cell.

[0086] For example, taking the first key performance indicator as an example, for each grid in each grid within a cell, if the number of gridded measurement reports corresponding to the grid is greater than or equal to a fourth threshold value, and the proportion of measurement reports with overlapping coverage in the gridded measurement reports corresponding to the grid is greater than or equal to a fifth threshold value, it is determined that the grid has overlapping coverage. Next, the proportion of grids with overlapping coverage in the grid with the cell as the main grid in the grid within the cell is counted, and based on the proportion, the key performance indicator of the cell is determined.

[0087] For the relevant description of determining whether there is overlapping coverage in the measurement report, please refer to the relevant description of determining whether there is overlapping coverage in the measurement report based on the first key performance indicator of the measurement report above, which will not be repeated in this disclosure. In at least one gridded measurement report corresponding to the grid, the grid with the largest proportion of measurement reports based on a certain cell as the main service cell is called the grid with the cell as the main grid.

[0088] In S202, a regional user perception index is determined based on the key performance indicators of each cell in the target area.

[0089] As an implementation method, the average value of the key performance indicators of each cell in the target area can be used as the regional user perception indicator.

[0090] As another implementation manner, the key performance indicator with the highest proportion among the key performance indicators of the cells in the target area may be used as the regional user perception indicator.

[0091] For example, there are five cells in the target area, namely cell 1, cell 2, cell 3, cell 4, and cell 5. The key performance indicator of cell 1 is indicator A, the key performance indicator of cell 2 is indicator B, the key performance indicator of cell 3 is indicator C, the key performance indicator of cell 4 is indicator A, and the key performance indicator of cell 5 is indicator A. The key performance indicator with the highest proportion of key performance indicators of each cell in the target area is indicator A, so indicator A is used as the regional user perception indicator.

[0092] As another implementation, as shown in FIG8 , the regional user perception index is determined according to the key performance index of each cell in the target area, which can be implemented as the following S701 and S702 .

[0093] In S701 , a cell-level user perception indicator of each cell in the target area is determined based on the key performance indicator of each cell in the target area.

[0094] The cell-level user perception indicator is used to characterize the perceived quality of experience of users in a cell for wireless communication services.

[0095] In some embodiments, based on the key performance indicators of each cell in the target area and the cell perception twin model, the cell-level user perception indicators of each cell in the target area are determined.

[0096] The cell perception twin model is used to determine the cell-level user perception indicators of a cell based on the key performance indicators of a cell.

[0097] Exemplarily, the key performance indicators of each cell in the target area are input into the cell perception twin model to obtain the cell-level user perception indicators of each cell in the target area output by the cell perception twin model.

[0098] As an implementation method, the cell perception twin model is trained based on the key performance indicators collected by the cell in the existing network and the cell-level user perception indicators collected by the cell in the existing network.

[0099] For example, as shown in Figure 9, there is a training flow chart for the cell perception twin model. First, the cell collects measurement reports in the existing network and determines the key performance indicators of the measurement reports based on the collected measurement reports. Then, based on the key performance indicators of the measurement reports, the key performance indicators of each cell in the target area are determined. Finally, the key performance indicators of each cell are used as features, and the cell-level user perception indicators collected by the cell in the existing network are used as labels. They are input into the composite model established in advance to train the composite model and obtain the hyperparameters defined by the cell perception twin model and the key performance indicators of the cell.

[0100] Exemplarily, the steps for constructing a composite model are as follows. First, combining the business knowledge of the cell's key performance indicators, a composite model is constructed from the measurement report to the cell's key performance indicators and then to the cell-level user perception indicators. The relevant part of the composite model from the measurement report to the cell's key performance indicators is constructed based on the business knowledge of the cell's key performance indicators, and there are some hyperparameters that need to be obtained through data training. The relevant part of the composite model from the cell's key performance indicators to the cell-level user perception indicators is a typical regression problem. Therefore, the composite model is a regression problem model with parameters, and the composite model needs to be trained using parameter optimization and regression methods.

[0101] It should be noted that determining the cell-level user perception index based on the key performance indicators of the cell is a typical regression problem. However, in the calculation process of the key performance indicators of the cell, there are hyperparameters that affect the calculation of the key performance indicators of the cell, and thus affect the determination of the cell-level user perception indicators. Therefore, for the optimization of the above hyperparameters, the above hyperparameters can be used as the parameters to be optimized, and the root mean square minimization of the cell perception twin model trained under the specified hyperparameters on the verification data set is used as the optimization goal to obtain the optimal parameters as the hyperparameters for calculating the key performance indicators of the cell, and the regression model trained under the secondary hyperparameters is used as the cell perception twin model.

[0102] In another exemplary embodiment, the construction formula of the cell perception twin model includes at least one of the following items: p = G(R, s) = G(F1(R, s), F2(R, s), F3(R, s)); F1(R, s) = [f 11 (R,s;c 11 ),…,f 1i (R,s;c 1i ),…,f 1I (R,s;c 1I )]; F2(R,s)=[f 21 (R,s;c 21 ),…,f 2j (R,s;c 2j ),…,f 2J (R,s;c 2J )]; F3(R,s)=[f 31 (R,s;c 31 ),…,f 3k (R,s;c 3k ),…,f 3K (R,s;c 3K )];

[0103] In the formula, p is a cell-level user perception indicator, such as the packet loss rate of the uplink real-time transport protocol (RTP); G is a cell perception twin model based on the key performance indicators of the cell; R is a plurality of measurement reports in the cell, including the RSRP of the primary service cell, the RSRP of the neighboring cell, the physical resource blocks of the physical uplink shared channel transmission, the physical resource blocks of the physical downlink shared channel transmission, etc.; s is the primary service cell corresponding to each measurement report in the multiple measurement reports; F1 is a cell perception twin model based on the first key performance indicator among the key performance indicators of the cell, including I first key performance indicators, f 1i (R,s;c 1i ), i∈[1,I], I is an integer, f 1iTake R and s as input and carry hyperparameter c 1i ; F2 is a cell perception twin model based on the second key performance indicator among the key performance indicators of the cell, including J second key performance indicators, f 2j (R,s;c 2j ), j∈[1,J], J is an integer, f 1i Take R and s as input and carry hyperparameter c 2j ; F3 is a cell perception twin model based on the third key performance indicator among the key performance indicators of the cell, including K third key performance indicators, f 3K (R,s;c 3K ), k∈[1,K], K is an integer, f 1i Take R and s as input and carry hyperparameter c 3k .

[0104] As shown in Figure 10, it is a flow chart for determining cell-level user perception indicators according to an embodiment of the present disclosure. First, multiple measurement reports of the target area, the initial values ​​of the antenna parameters of each cell in the target area, and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process are obtained, and multiple measurement reports are updated to obtain multiple updated measurement reports. Then, based on the multiple updated measurement reports, the key performance indicators in the multiple updated measurement reports are determined, and based on the key performance indicators in the multiple updated measurement reports, the key performance indicators of each cell in the target area are determined. Finally, the key performance indicators of each cell in the target area are input into the cell perception twin model to obtain the cell-level user perception indicators of each cell in the target area.

[0105] In the process of determining the cell-level user perception index of each cell in the target area, the following formula is used: p = G(R′,s′) = G(F1(R′,s′),F2(R′,s′),F3(R′,s′)); R′ = p(R,x); s′ = α(R′);

[0106] Where x represents the candidate value of the antenna parameter of each cell in the optimization process; R′ is the RSRP of the primary serving cell and the RSRP of the neighbor cell in each measurement report of multiple updated measurement reports; s′ represents the primary serving cell in each measurement report of multiple updated measurement reports.

[0107] In S702 , a regional user perception index is determined based on the cell-level user perception index of each cell in the target area.

[0108] As an implementation method, the average value of the cell-level user perception indicators of each cell in the target area may be used as the area-level user perception indicator.

[0109] As another implementation manner, the cell-level user perception indicator with the highest proportion among the cell-level user perception indicators of each cell in the target area may be used as the regional-level user perception indicator.

[0110] The determination method may refer to the above description of taking the key performance indicator with the highest proportion among the key performance indicators of each cell in the target area as the regional user perception indicator, which will not be elaborated in this disclosure.

[0111] As shown in Figure 11, a flow chart of an antenna parameter optimization method according to an embodiment of the present disclosure is provided. First, multiple measurement reports of the target area, the initial values ​​of the antenna parameters of each cell in the target area, and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process are obtained. Through the process of determining the cell-level user perception index, the cell-level user perception index of each cell in the target area is obtained. Next, based on the cell-level user perception index of each cell in the target area, the regional user perception index is determined. Finally, based on the regional user perception index and combined with the intelligent optimization algorithm, the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process are iteratively updated.

[0112] Figure 12 shows a flow chart of another antenna parameter optimization method according to an embodiment of the present disclosure. First, data is collected from the existing network, including multiple measurement reports for the target area and the initial values ​​of the antenna parameters for each cell in the target area. The regional user perception index is then determined based on the candidate values ​​of the antenna parameters for each cell in the target area during the optimization process. Next, based on the regional user perception index and in conjunction with an intelligent optimization algorithm, the candidate values ​​of the antenna parameters for each cell in the target area during the optimization process are iteratively updated. Ultimately, the candidate values ​​of the antenna parameters for each cell in the target area during the optimization process are fed back to the existing network.

[0113] In this way, by using the regional user perception index that characterizes the perceived experience quality of wireless communication services for users in the target area as the optimization target of the antenna parameters in the target area, the antenna parameters can be effectively optimized based on the user's perceived experience, so that the communication service can better meet the user's needs and improve the user's communication service experience.

[0114] The embodiment of the present disclosure can divide the electronic device into functional modules according to the above-mentioned method embodiment. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one functional module. The above-mentioned integrated module can be implemented in the form of hardware or software. It should be noted that the division of modules in the embodiment of the present disclosure is schematic and is only a logical function division. There may be other division methods in actual implementation. The following is an example of dividing each functional module corresponding to each function.

[0115] FIG13 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. The electronic device 130 can execute the antenna parameter optimization method provided by the above method embodiment. As shown in FIG13 , the electronic device 130 includes: an acquisition module 1301 and a processing module 1302.

[0116] The acquisition module 1301 acquires the initial value of the antenna parameter of each cell in the target area and multiple measurement reports of the target area.

[0117] Processing module 1302 optimizes the antenna parameters of each cell in the target area based on the initial values ​​of the antenna parameters of each cell in the target area and multiple measurement reports for the target area, with the goal of optimizing a regional user perception index (UEI), and determines target values ​​for the antenna parameters of each cell in the target area. The regional user perception index represents the perceived quality of experience of wireless communication services for users in the target area.

[0118] In some embodiments, the regional user perception index is determined according to the following steps: determining the key performance indicators of each cell in the target area based on the initial values ​​of the antenna parameters of each cell in the target area, multiple measurement reports of the target area, and candidate values ​​of the antenna parameters of each cell in the target area during the optimization process; determining the regional user perception index based on the key performance indicators of each cell in the target area.

[0119] In some embodiments, key performance indicators of each cell in the target area are determined based on the initial values ​​of the antenna parameters of each cell in the target area, multiple measurement reports of the target area, and candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, including: updating multiple measurement reports based on the initial values ​​of the antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process to obtain multiple updated measurement reports; and determining the key performance indicators of each cell in the target area based on the multiple updated measurement reports.

[0120] In some embodiments, the measurement report includes an identifier of a primary serving cell and measurement information of at least one cell, the at least one cell including a serving cell and a neighboring cell, and the primary serving cell is one of the serving cells. Based on the initial values ​​of antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, multiple measurement reports are updated to obtain multiple updated measurement reports, including: for each measurement report in the multiple measurement reports, based on the initial values ​​of the antenna parameters of each cell in the target area and the candidate values ​​of the antenna parameters of each cell in the target area during the optimization process, updating the measurement information of at least one cell in the measurement report to obtain updated measurement information of the at least one cell; re-determining the primary serving cell corresponding to the measurement report based on the updated measurement information of the at least one cell; and generating an updated measurement report based on the identifier of the re-determined primary serving cell and the updated measurement information of the at least one cell.

[0121] In some embodiments, based on multiple updated measurement reports, key performance indicators of each cell in the target area are determined, including: for each cell in the target area, determining at least one updated measurement report with the cell as the main serving cell from multiple updated measurement reports; based on at least one updated measurement report with the cell as the main serving cell, determining the key performance indicators of the cell.

[0122] In some embodiments, determining the key performance indicators of a cell based on at least one updated measurement report of the cell as the primary service cell includes: determining the key performance indicators of each grid within the cell based on at least one updated measurement report of the cell as the primary service cell; determining the key performance indicators of the cell based on the key performance indicators of each grid within the cell.

[0123] In some embodiments, regional-level user perception indicators are determined based on key performance indicators of each cell in the target area, including: determining cell-level user perception indicators of each cell in the target area based on the key performance indicators of each cell in the target area, where the cell-level user perception indicators are used to characterize the perceived experience quality of users in a cell for wireless communication services; and determining regional-level user perception indicators based on the cell-level user perception indicators of each cell in the target area.

[0124] In some embodiments, based on the key performance indicators of each cell in the target area, the cell-level user perception indicators of each cell in the target area are determined, including: based on the key performance indicators of each cell in the target area and the cell perception twin model, the cell-level user perception indicators of each cell in the target area are determined, and the cell perception twin model is used to determine the cell-level user perception indicators of a cell based on the key performance indicators of a cell.

[0125] In some embodiments, the cell perception twin model is trained based on key performance indicators collected by the cell in the existing network and cell-level user perception indicators collected by the cell in the existing network.

[0126] In some embodiments, the key performance indicators include at least one of the following: a first key performance indicator, a second key performance indicator, and a third key performance indicator. The first key performance indicator is used to characterize network coverage performance; the second key performance indicator is used to characterize network capacity; and the third key performance indicator is used to characterize network interference level.

[0127] In some embodiments, the antenna parameter includes at least one of the following: antenna power, antenna downtilt angle.

[0128] In the case of implementing the functions of the above-mentioned integrated modules in hardware, the embodiments of the present disclosure provide another structure of the electronic device involved in the above-mentioned embodiments. As shown in Figure 14, the electronic device 140 includes: a processor 1402 and a bus 1404. As an implementation method, the electronic device may also include a memory 1401. As an implementation method, the electronic device may also include a communication interface 1403.

[0129] Processor 1402 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present disclosure. Processor 1402 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof, and may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of the present disclosure. Processor 1402 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP (digital signal processor) and a microprocessor, and the like.

[0130] The communication interface 1403 is used to connect to other devices via a communication network, such as Ethernet, wireless access network, or wireless local area network (WLAN).

[0131] The memory 1401 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0132] As an implementation, memory 1401 may exist independently of processor 1402. Memory 1401 may be connected to processor 1402 via bus 1404 to store instructions or program codes. When processor 1402 calls and executes the instructions or program codes stored in memory 1401, the antenna parameter optimization method provided in the embodiments of the present disclosure can be implemented.

[0133] In another implementation, the memory 1401 may also be integrated with the processor 1402 .

[0134] Bus 1404 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 1404 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, FIG14 shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0135] Some embodiments of the present disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium), which stores computer program instructions. When the computer program instructions are executed on a computer, the computer executes the antenna parameter optimization method described in any of the above embodiments.

[0136] Exemplarily, the above-mentioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes, etc.), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memories (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in the present disclosure may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0137] An embodiment of the present disclosure provides a computer program product comprising instructions. When the computer program product is run on a computer, the computer is enabled to execute the antenna parameter optimization method described in any one of the above embodiments.

[0138] In the embodiment of the present disclosure, by using a regional user perception index that characterizes the perceived experience quality of wireless communication services by users in a target area as an optimization target for antenna parameters in the target area, the antenna parameters can be effectively optimized based on the user's perceived experience, thereby enabling the communication service to better meet the user's needs and improve the user's communication service experience.

[0139] The above is only a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or replacements within the technical scope disclosed in the present disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. An optimization method for antenna parameters, comprising: Obtaining initial values of antenna parameters of each cell in a target area and a plurality of measurement reports of the target area; Based on the initial values of the antenna parameters of each cell in the target area and the plurality of measurement reports of the target area, aiming at the optimal regional user perception index, optimizing the antenna parameters of each cell in the target area to determine target values of the antenna parameters of each cell in the target area; Wherein, the regional user perception index is used to characterize the perceived quality of experience of users in the target area for wireless communication services.

2. The method according to claim 1, wherein The regional user perception index is determined according to the following steps: Determining key performance indicators of each cell in the target area according to the initial values of the antenna parameters of each cell in the target area, the plurality of measurement reports of the target area, and candidate values of the antenna parameters of each cell in the target area during the optimization process; Determining the regional user perception index according to the key performance indicators of each cell in the target area.

3. The method according to claim 2, wherein The determining the key performance indicators of each cell in the target area according to the initial values of the antenna parameters of each cell in the target area, the plurality of measurement reports of the target area, and candidate values of the antenna parameters of each cell in the target area during the optimization process includes: Updating the plurality of measurement reports according to the initial values of the antenna parameters of each cell in the target area and the candidate values of the antenna parameters of each cell in the target area during the optimization process to obtain a plurality of updated measurement reports; Based on the plurality of updated measurement reports, determining the key performance indicators of each cell in the target area.

4. The method according to claim 3, wherein, Each of the plurality of measurement reports includes an identifier of a primary serving cell and measurement information of at least one cell, the at least one cell includes a serving cell and neighbor cells, and the primary serving cell is one of the serving cells; The updating the plurality of measurement reports according to the initial values of the antenna parameters of each cell in the target area and the candidate values of the antenna parameters of each cell in the target area during the optimization process to obtain a plurality of updated measurement reports includes: For each of the plurality of measurement reports, updating the measurement information of at least one cell in each of the plurality of measurement reports according to the initial values of the antenna parameters of each cell in the target area and the candidate values of the antenna parameters of each cell in the target area during the optimization process to obtain updated measurement information of the at least one cell; Based on the updated measurement information of the at least one cell, re-determining the primary serving cell corresponding to each of the plurality of measurement reports; Generating each of the updated plurality of measurement reports based on the identifier of the re-determined primary serving cell and the updated measurement information of the at least one cell.

5. The method according to claim 3, wherein The determining the key performance indicators of each cell in the target area based on the plurality of updated measurement reports includes: For each cell in the target area, determine at least one updated measurement report with the cell as the primary serving cell from the multiple updated measurement reports; Based on at least one updated measurement report with the cell as the primary serving cell, determine the key performance indicators of the cell.

6. The method according to claim 5, wherein The determining of the key performance indicators of the cell based on at least one updated measurement report with the cell as the primary serving cell includes: Based on at least one updated measurement report with the cell as the primary serving cell, determine the key performance indicators of each grid within the cell; Based on the key performance indicators of each grid within the cell, determine the key performance indicators of the cell.

7. The method according to claim 2, wherein The determining of the regional-level user perception indicator according to the key performance indicators of each cell in the target area includes: Based on the key performance indicators of each cell in the target area, determine the cell-level user perception indicator of each cell in the target area, where the cell-level user perception indicator is used to characterize the perceived experience quality of users within a cell for wireless communication services; According to the cell-level user perception indicators of each cell in the target area, determine the regional-level user perception indicator.

8. The method according to claim 7, wherein The determining of the cell-level user perception indicator of each cell in the target area based on the key performance indicators of each cell in the target area includes: Based on the key performance indicators of each cell in the target area and the cell perception twin model, determine the cell-level user perception indicator of each cell in the target area, where the cell perception twin model is used to determine the cell-level user perception indicator of a cell based on the key performance indicators of the cell.

9. The method according to claim 8, wherein, The cell perception twin model is trained based on the key performance indicators collected by the cell in the existing network and the cell-level user perception indicators collected by the cell in the existing network.

10. The method according to claim 2, wherein, The key performance indicators include at least one of the following: a first key performance indicator, a second key performance indicator, and a third key performance indicator; where the first key performance indicator is used to characterize the network coverage performance; the second key performance indicator is used to characterize the network capacity; the third key performance indicator is used to characterize the network interference level.

11. The method according to claim 1, wherein, The antenna parameters include at least one of the following: antenna power, antenna downtilt.

12. An electronic device, including a processor, wherein, When the processor executes the computer program, it implements the optimization method of the antenna parameters according to any one of claims 1 to 11.

13. A computer-readable storage medium, wherein, The computer-readable storage medium includes computer instructions; where when the computer instructions are executed, it implements the optimization method of the antenna parameters according to any one of claims 1 to 11.

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