An antenna feeder parameter optimization method, device, network device and storage medium

Abstract

An embodiment of the present invention provides a method, device, network device, and storage medium for optimizing antenna feeder parameters. In this antenna feeder parameter optimization solution, after determining that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, an adjustment cell is selected, and then the target antenna feeder parameters for the adjustment cell are automatically determined, and the antenna feeder parameters of the adjustment cell are adjusted according to the target antenna feeder parameters. After the adjustment, the adjustment effect will be further evaluated to avoid the negative impact on VoLTE caused by improper adjustment. The processes of problem location, problem optimization, and optimization evaluation are fully automated without manual participation, which is beneficial to reducing the network operation and maintenance costs. At the same time, the automated antenna feeder parameter optimization solution has high optimization efficiency, can solve problems in a timely manner after VoLTE problems occur, reduce the impact of VoLTE problems on users' voice services, and is beneficial to improving the user experience.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular, to a method and apparatus for optimizing antenna feeder parameters, a network device, and a storage medium. Background Art

[0002] VoLTE (Voice over Long-Term Evolution) is an end-to-end voice solution under full IP conditions based on the LTE network. It is the basis for rich media converged communication and the key to high-quality voice services in the LTE era. VoLTE can bring commercial values such as reducing network costs and improving user perception to operators.

[0003] With the successive commercial deployment of VoLTE and the launch of VoLTE commercial services by various operators across the country, customers' requirements for the overall service quality of the network are continuously increasing. However, currently, there are still problems that affect user perception, such as single-channel communication, word swallowing, and call dropping caused by radio interface reasons during the call stage. VoLTE has different characteristics from LTE. Therefore, if traditional manual solutions for solving LTE radio side problems are used to solve VoLTE radio side problems, new problems may occur. More importantly, traditional manual solutions rely on manual problem troubleshooting and problem solving, so the problem-solving efficiency is low. Once a problem occurs, it will affect the user's VoLTE voice service experience for a long time. Moreover, traditional manual solutions have high operation and maintenance costs and are not conducive to the optimal allocation of resources. Summary of the Invention

[0004] The method, apparatus, network device, and storage medium for optimizing antenna feeder parameters provided in the embodiments of the present invention mainly solve the technical problems of low efficiency and high operation and maintenance costs in the related art when using manual solutions for VoLTE voice services.

[0005] To solve the above technical problems, an embodiment of the present invention provides a method for optimizing antenna feeder parameters, including:

[0006] Determine that the reason for the poor VoLTE (voice service based on the IP multimedia subsystem) quality of the cell to be optimized is one of the preset reasons;

[0007] Determine the adjustment cell for this optimization from the cells to be optimized, and the adjustment cell has a non-strong neighboring cell relationship with other adjustment cells within this adjustment period;

[0008] Determine the target antenna feeder parameters of the adjustment cell, and adjust the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters;

[0009] Evaluate the effect of this adjustment. If the evaluation result indicates that the adjustment of the adjustment cell is effective, then maintain the adjustment of the adjustment cell.

[0010] An embodiment of the present invention further provides an antenna feeder parameter optimization device, including:

[0011] A problem location module, configured to determine that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons;

[0012] A cell selection module, configured to determine an adjustment cell for this optimization from the cells to be optimized, and the adjustment cell has a non-strong neighboring cell relationship with other adjustment cells within this adjustment period;

[0013] A parameter optimization module, configured to determine the target antenna feeder parameters of the adjustment cell, and adjust the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters;

[0014] An effect evaluation module, configured to evaluate the effect of this adjustment. If the evaluation result indicates that the adjustment of the adjustment cell is effective, the adjustment of the adjustment cell is maintained.

[0015] An embodiment of the present invention further provides a network device, which includes a processor, a memory, and a communication bus;

[0016] The communication bus is used to realize the connection communication between the processor and the memory;

[0017] The processor is configured to execute one or more programs stored in the memory to implement the steps of the above antenna feeder parameter optimization method.

[0018] An embodiment of the present invention further provides a computer storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the above antenna feeder parameter optimization method.

[0019] The beneficial effects of the present invention are:

[0020] The antenna feed parameter optimization method, device, network device and storage medium provided by the embodiments of the present invention determine that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, then determine the adjustment cell for this optimization from the cells to be optimized, determine the target antenna feed parameters of the adjustment cell, and adjust the antenna feed parameters of the adjustment cell according to the target antenna feed parameters. Subsequently, the effect of this adjustment is evaluated. If the evaluation result indicates that this adjustment is effective, this adjustment is maintained; otherwise, this adjustment is rolled back. In this antenna feed parameter optimization scheme, after determining that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, the adjustment cell is selected, and then the target antenna feed parameters for the adjustment cell are automatically determined, and the antenna feed parameters of the adjustment cell are adjusted according to the target antenna feed parameters. After the adjustment, the adjustment effect will be further evaluated to avoid the negative impact on VoLTE caused by improper adjustment. The processes of problem location, problem optimization and optimization evaluation are fully automated without manual participation, which is beneficial to reducing the network operation and maintenance cost. At the same time, the automated antenna feed parameter optimization scheme has high optimization efficiency, can solve problems in time after VoLTE problems occur, reduce the impact of VoLTE problems on users' voice services, and is beneficial to improving the user experience.

[0021] Other features and corresponding beneficial effects of the present invention are described in the following part of the specification, and it should be understood that at least some of the beneficial effects are obvious from the description in the specification of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the antenna feed parameter optimization method provided in the first embodiment of the present invention;

[0023] Figure 2 It is a flowchart of the network device determining the adjustment cell shown in the first embodiment of the present invention;

[0024] Figure 3 It is a flowchart of the network device determining the adjustment cell set from the cells to be optimized provided in the first embodiment of the present invention;

[0025] Figure 4 It is a flowchart of the antenna feed parameter optimization method provided in the second embodiment of the present invention;

[0026] Figure 5 It is a flowchart of the network device evaluating the evaluation effect of the adjustment cell provided in the second embodiment of the present invention;

[0027] Figure 6 It is a flowchart of the network device determining the evaluation area of the adjustment cell provided in the second embodiment of the present invention;

[0028] Figure 7It is a schematic structural diagram of an antenna feeder parameter optimization device provided in Embodiment 3 of the present invention;

[0029] Figure 8 It is another schematic structural diagram of an antenna feeder parameter optimization device provided in Embodiment 3 of the present invention;

[0030] Figure 9 It is a schematic hardware structure diagram of a network device provided in Embodiment 5 of the present invention. Detailed implementation manners

[0031] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the embodiments of the present invention will be further described in detail below through specific implementation manners in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0032] Embodiment 1:

[0033] In order to overcome the defects in the related art that when manually solving the VoLTE radio side problems in the way of solving LTE radio side problems, the problem-solving efficiency is low, the network operation and maintenance cost is high, and new problems may be introduced, this embodiment provides an antenna feeder parameter optimization method. Please refer to Figure 1 A flowchart showing an antenna feeder parameter optimization method:

[0034] S102: Determine whether the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons.

[0035] If the judgment result is yes, that is, the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, then S104 can be executed to perform antenna feeder parameter optimization in the manner provided in this embodiment. Otherwise, it is not suitable to use the antenna feeder parameter optimization solution provided in this embodiment to solve the problem. Therefore, the process can be ended.

[0036] The so-called cell to be optimized refers to the cell with poor current VoLTE quality that needs to be optimized. Usually, there can be multiple cells to be optimized, but this is not a limitation, but because in some cases, there can be only one cell to be optimized.

[0037] In some examples of this embodiment, the cell to be optimized is automatically selected by the network device. For example, in some examples, the network device can evaluate the VoLTE quality of each cell in a predetermined area, and thus select the cells with poor voice service quality as the cells to be optimized. For example, the network device can determine the MOS values of each cell in the predetermined area first, and then determine the cells to be optimized according to the MOS values of each cell. In one example, the network device can obtain the VoLTE data and MR data of all cells in the predetermined area, then correlate the VoLTE data and MR data of a cell, and calculate the MOS value at the slice level (5 seconds as a slice). After calculating the MOS of each cell, the network device determines whether the MOS value of each slice is of poor quality, and respectively determines the MOS poor-quality ratio of each cell (that is, the number of slice MOS values of poor quality in the cell / the number of all slice MOS values in the cell), and determines the cells with the top 1 / 3 MOS poor-quality ratio as the cells to be optimized. Assume that there are six cells a, b, c, d, e, and f in the predetermined area. Then when the network device selects the cells to be optimized, it will select 1 / 3 of the cells from these six cells, that is, two cells. If the cells with the first and second MOS poor-quality ratios among these six cells are c and a respectively, then the cells to be optimized determined by the network device are a and c.

[0038] In some other examples of this embodiment, the cell to be optimized can be reported manually by other devices or network management personnel. Because when the VoLTE quality of a certain cell is poor, the voice services of the users in that cell will surely be affected and can be perceived by the users. The network management personnel can specify the cell to be optimized to the network device according to user complaints, etc.

[0039] The so-called preset reasons in this embodiment include but are not limited to coverage reasons, interference reasons, and capacity reasons. If, after the judgment of the network device, it is determined that the reason for the poor VoLTE quality of the cell to be optimized is one of the above three reasons, then the VoLTE quality optimization of the cell to be optimized can be achieved through this case. It can be understood that not necessarily all the reasons for the poor VoLTE quality of the cells to be optimized are the same. For example, in one example of this embodiment, the poor VoLTE quality of cell a to be optimized is due to coverage reasons, while cell c to be optimized is due to interference problems.

[0040] S104: Determine the adjustment cell for this optimization from the cells to be optimized.

[0041] After determining that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, the network device can select the adjustment cell for this optimization from the cells to be optimized. Generally, within one adjustment period, one or more adjustment cells in the cells to be optimized can be adjusted, and each adjustment cell in the same adjustment period can form an "adjustment cell set". However, in order to avoid adjusting two adjacent cells within the same period, resulting in a poor optimization effect, there is no strong neighbor relationship between the cells belonging to the same adjustment cell set in this embodiment. For a cell, its strong neighbor refers to a neighbor cell with a relatively strong correlation with this cell:

[0042] In some examples of this embodiment, when determining whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, it can be determined based on the MR of this cell, and it is determined whether the frequency of occurrence of this neighbor cell in the MR of this cell is high enough. If so, it is its strong neighbor cell; otherwise, it is not its strong neighbor cell. In one example of this embodiment, only when the proportion of a neighbor cell appearing in the MR of a cell reaches the preset strong neighbor proportion will this neighbor cell be determined as the strong neighbor cell of the corresponding cell. That is, when the number of MRs of a cell is certain, only when the number of times a neighbor cell appears in it reaches the preset strong neighbor threshold will it be recognized as the strong neighbor cell of the corresponding cell. For example, in some examples, the proportion of the strong neighbor cell of a cell a appearing in the MR of a cell a must reach 5%. If the terminals in cell a have reported a total of 100 MRs, then if a cell is a strong neighbor cell of this cell a, the number of times this cell appears in the MR of cell a is at least 5 times.

[0043] In some other examples of this embodiment, when determining whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, the proportion of all neighbor cells of this cell appearing in the MR of this cell can be determined, and then the top k neighbor cells are selected as strong neighbor cells. For example, a cell b has 80 neighbor cells, and 10 of them will be determined as strong neighbor cells. Then the frequencies or proportions of these 80 neighbor cells appearing in the MR of cell b can be determined respectively, and then the top 10 neighbor cells with higher frequencies or proportions of appearance are selected as the strong neighbor cells of cell b.

[0044] In some examples of this embodiment, when the network device determines the adjustment cell in this adjustment from the optimized cells, it can first determine the adjustment cell set. Please refer to Figure 2 A flowchart showing how the network device determines the adjustment cell:

[0045] S202: Determine the set of adjustment cells to be optimized within this adjustment period from the cells to be optimized.

[0046] In some examples of this embodiment, when the network device determines the adjustment cell set corresponding to the current adjustment period from the cells to be optimized, it only needs to ensure that there is no strong neighbor cell relationship among the cells in the adjustment cell set. In this case, the network device can determine the cells to be adjusted according to the Figure 3 flow chart shown below:

[0047] S302: Select a cell from the cells to be optimized and add it to the adjustment cell set;

[0048] The network device can first randomly select a cell from the cells to be optimized and add it to the adjustment cell set.

[0049] S304: Select a cell that has no strong neighbor cell relationship with the cells in the adjustment cell set from the remaining cells to be optimized and add it to the adjustment cell set;

[0050] S306: Determine whether there are still cells in the cells to be optimized that have no strong neighbor cell relationship with the cells in the adjustment cell set;

[0051] If the judgment result is yes, continue to execute S304. Otherwise, it means that the adjustment cell set for this adjustment period has been selected. Therefore, the process ends.

[0052] Of course, in some examples of this embodiment, the network device can also limit the selection and determination of the adjustment cell set by the number. For example, if the number of cells in the adjustment cell set cannot exceed 6, then when the network device selects the adjustment cells according to the Figure 3 flow chart shown below, although there are still other eligible cells in the cells to be optimized, but because the number of cells in the current adjustment cell set has reached 6, the network device will not continue to select.

[0053] In some examples of this embodiment, not only is it required that there is no strong neighbor cell relationship among the cells selected by the network device in the adjustment cell set, but also the impact brought about after the selected adjustment cells are adjusted will be considered. Generally, if the number of users in a cell is large, then when the VoLTE quality of this cell is poor, it will cause a large number of users to have a bad voice service experience. Therefore, the network device should give priority to ensuring the VoLTE quality of such cells with a large number of users or a wide coverage area. Therefore, in some examples of this embodiment, when determining the adjustment cell set, the network device can preferentially select the cells with a large number of users in the cells to be optimized. For example, in one example of this embodiment, the network device can select the top n cells with a large reported MR value or the cells with a reported MR value greater than m to form the adjustment cell set corresponding to this adjustment period.

[0054] In addition, if the VoLTE quality of a cell is very poor, optimizing such a cell as soon as possible also has a good optimization effect. Therefore, in an example of this embodiment, when determining the adjusted cell set, the network device preferentially selects cells with a lower average MOS value of the cell. For example, the network device selects cells with an average MOS value lower than the preset average value to form the adjusted cell set corresponding to this adjustment period. The average MOS value of the cell mentioned here refers to the average MOS value determined based on at least two MOS values of a cell.

[0055] Of course, the network device can also combine the above two principles to select cells with a larger coverage area and relatively poor original VoLTE quality to form the adjusted cell set. For example, in an example of this embodiment, the network device can first select the top n cells with a larger reported MR number or cells with a reported MR number greater than m from each cell to be optimized to form the first cell set, and then select cells with an average MOS value lower than the preset average value from the first cell set to form the adjusted cell set corresponding to this adjustment period. In another example of this embodiment, the network device can first select cells with an average MOS value lower than the preset average value from each cell to be optimized to form the second cell set, and then select the top n cells with a larger reported MR number or cells with a reported MR number greater than m from the second cell set to form the adjusted cell set corresponding to this adjustment period. Those skilled in the art can understand that the network device can also select the first cell set and the second cell set at the same time, and then determine the intersection cells of the first cell set and the second cell set to form the adjusted cell set.

[0056] S204: Determine the adjusted cell for this optimization from the adjusted cell set.

[0057] After determining the adjusted cell set, when the network device determines the adjusted cell from the adjusted cell set, it can randomly select an unadjusted cell from it.

[0058] It can be understood that the network device can only adjust the antenna parameters of one adjusted cell in each adjustment process. However, because the network device needs to interact with the base station in each adjustment process, if only one adjusted cell in the adjusted cell set is adjusted in each adjustment process, when there are multiple adjusted cells in the adjusted cell set, the network device needs to interact with the base station multiple times. Therefore, in some examples of this embodiment, the network device can select two or more adjusted cells from the adjusted cell set in each adjustment process. For example, in an example of this embodiment, the network device can adjust all the adjusted cells in the adjusted cell set in one adjustment process.

[0059] In the above example, the network device first determines an adjustment cell set from the cells to be optimized, and then selects an adjustment cell for this adjustment from the adjustment cell set. However, in some other examples of this embodiment, the network device can directly select an adjustment cell from the cells to be optimized without first determining the adjustment cell set. In such an example, the network device also needs to ensure that there is no strong neighbor cell relationship between the selected adjustment cells in the same adjustment period. For example, if in a certain adjustment process, the network device selects two cells as adjustment cells from the cells to be optimized, on the one hand, the network device needs to ensure that these two cells and the adjustment cells selected in other adjustment processes in this adjustment period are not in a strong neighbor cell relationship, and on the other hand, the network device also needs to ensure that the two cells selected this time are not in a strong neighbor cell relationship with each other.

[0060] S106: Determine the target antenna feed parameters of the adjustment cell, and adjust the antenna feed parameters of the adjustment cell according to the target antenna feed parameters.

[0061] After determining the adjustment cell for this adjustment, the network device can determine the target antenna feed parameters of the adjustment cell. The so-called target antenna feed parameters are the antenna feed parameters that can make the VoLTE quality relatively ideal determined according to the relevant performance, attributes, etc. of the adjustment cell. Optionally, the network device can determine the target antenna feed parameters of the adjustment cell according to at least one of the inherent attribute parameters and performance index parameters of the adjustment cell. The inherent attribute parameters mentioned here include but are not limited to the station height of the adjustment cell. The performance index parameters include at least one of the following parameters: RSRP (Reference Signal Received Power), TA (Timing Advance), over-coverage, and weak coverage. Of course, those skilled in the art can understand that the performance index parameters can also be other parameters.

[0062] After determining the target antenna feed parameters of the adjustment cell, the network device can adjust the antenna feed parameters of the adjustment cell according to the target antenna feed parameters. Usually, the network device can send an adjustment instruction to the base station, and the adjustment instruction sent contains the target antenna feed parameters of the adjustment cell. In this way, when the base station receives the adjustment instruction sent by the network device, it can adjust the antenna feed parameters of the corresponding cell according to the target antenna feed parameters therein.

[0063] It should be understood that before the network device adjusts the antenna feed parameters of the adjustment cell, it should first determine that the current antenna feed parameters of the adjustment cell are inconsistent with the determined target antenna feed parameters. Otherwise, if the current antenna feed parameters of the adjustment cell are consistent with the target antenna feed parameters, the network device has no need to send an adjustment instruction to the base station.

[0064] S108: Evaluate the effect of this adjustment. If the evaluation result indicates that this adjustment is effective, then maintain this adjustment.

[0065] After adjusting the antenna feeder parameters of the adjustment cell, the network device will also evaluate the effect of this adjustment. If the evaluation result indicates that this adjustment is effective, the network device will maintain this adjustment. The so-called effective adjustment here means that the VoLTE quality of the adjustment cell has improved after the adjustment. In some other examples of this embodiment, the so-called effective adjustment not only requires that the VoLTE quality of the adjustment cell has improved after being adjusted, but also has requirements for the improvement degree.

[0066] It can be understood that if the network device determines that its adjustment to a certain adjustment cell is effective, then the network device will not perform optimization adjustments on this adjustment cell in this adjustment cycle and subsequent other adjustment cycles. Therefore, the network device can delete this adjustment cell from the cells to be optimized.

[0067] If the network device determines through evaluation that this adjustment is ineffective, it can consider rolling back this adjustment, that is, restoring the antenna feeder parameters of the adjustment cell to the state before the adjustment. In some other examples of this embodiment, the network device will not roll back this adjustment. However, in either case, the network device will not let this adjustment cell with ineffective adjustment continue to remain in the cells to be optimized so as to continue to optimize this cell in the subsequent process.

[0068] The antenna feeder parameter optimization method provided in this embodiment can determine the cells to be optimized according to the MOS value conditions of each cell in a predetermined area, then select the adjustment cells that need to be optimized and adjusted this time from the cells to be optimized, and then determine the target antenna feeder parameters of the adjustment cells according to at least one of the inherent attribute parameters and performance index parameters of the adjustment cells. If the network device determines that the current antenna feeder parameters of this adjustment cell are inconsistent with the target antenna feeder parameters, it will send an adjustment instruction to the base station to let the base station adjust the antenna feeder parameters of the corresponding cell. And after the adjustment, the network device will also evaluate the evaluation effect, and determine whether to maintain the adjustment or roll back the adjustment according to the adjustment effect of the adjustment cell. The network device can automatically discover problems, analyze problems, locate the causes, solve problems and evaluate the effects on the VoLTE radio side, reducing the manual intervention process and improving the efficiency of wireless network optimization.

[0069] Embodiment 2:

[0070] This embodiment will introduce the effect evaluation process in the antenna feeder parameter optimization method on the basis of Embodiment 1. First, please refer to Figure 4 a flowchart of an antenna feeder parameter optimization method shown:

[0071] S402: Determine the cells to be optimized based on the MOS values of each cell in the predetermined area.

[0072] In this embodiment, the network device can determine the MOS value of each cell in the predetermined area, and then determine the cells to be optimized according to the MOS values of each cell. For example, the network device can calculate the slice-level MOS value, and then select the cells with MOS values lower than the preset MOS threshold as the cells to be optimized, or select a certain proportion of cells as the cells to be optimized in ascending order of MOS values. In some examples of this embodiment, the cells to be optimized can also be specified manually.

[0073] S404: Screen out the cells in the cells to be optimized whose reasons for poor VoLTE quality do not belong to the preset reasons.

[0074] It can be understood that when the network device analyzes and locates the reasons for poor VoLTE quality of the cells to be optimized on the radio side, it does not analyze all the cells in the cells to be optimized as a whole at the same time, but analyzes the cells therein separately. For example, for the cells to be optimized, it is possible that the reasons for poor VoLTE quality of some cells are one of the preset reasons, but it is also possible that the reasons for poor VoLTE quality of some other cells are not affected by the preset reasons. In this case, the problems of poor VoLTE quality of some cells to be optimized cannot be solved through the subsequent process results, but the problems of poor VoLTE quality of some cells to be optimized can be solved through the subsequent process.

[0075] S406: Determine the adjustment cell set for this adjustment period from the cells to be optimized.

[0076] Subsequently, the network device can determine the adjustment cell set corresponding to this adjustment period from the cells to be optimized. Optionally, when the network device selects each adjustment cell in this adjustment period from the cells to be optimized, it will not only ensure that the selected adjustment cells are not in a strong neighboring cell relationship, but also try to select the cells with relatively larger coverage ranges and relatively worse VoLTE quality. The process of selecting adjustment cells has been introduced in detail in the foregoing embodiments and will not be elaborated here. In addition, those skilled in the art can understand that when the network device selects the cells constituting the adjustment cell set, it can also select according to other principles.

[0077] S408: For each adjustment cell in the adjustment cell set, determine the target antenna feed parameters according to the inherent attribute parameters and performance index parameters of the adjustment cell.

[0078] In this embodiment, after the network device selects all the adjustment cells for an adjustment period, it can adjust these adjustment cells together. That is, for each adjustment cell within the same adjustment period, the network device no longer determines the target antenna feed parameters in batches. Therefore, after the network device determines the adjustment cell set, it determines the corresponding target antenna feed parameters according to the inherent attribute parameters and performance index parameters of each adjustment cell respectively.

[0079] S410: Adjust the adjustment cells according to the target antenna feed parameters.

[0080] After the network device determines the target antenna feed parameters corresponding to each adjustment cell, it sends these target antenna feed parameters to the base station through an adjustment instruction, so that the base station can obtain the target antenna feed parameters of each adjustment cell in the adjustment cell set according to this adjustment instruction, and complete the adjustment of these adjustment cells according to the target antenna feed parameters.

[0081] S412: Evaluate the adjustment effect of the adjustment cells.

[0082] [[ID=~12]]In some examples of this embodiment, when evaluating the adjustment effect of the adjustment cell, the network device can only evaluate each evaluation index that can reflect the VoLTE quality before and after the adjustment of the adjustment cell, and then compare whether the evaluation index after the adjustment is better than the evaluation index before the adjustment, so as to judge whether the current adjustment is effective.

[0083] However, since a cell supporting VoLTE is also carrying data traffic services at the same time, and generally speaking, in terms of coverage, the coverage of VoLTE (QCI (Channel Quality Indicator) is 1 or 2) is smaller than the coverage of LTE data traffic (QCI is 8 or 9). Therefore, if only the VoLTE effect is considered when evaluating the adjustment effect, it may cause problems such as poor signal or even coverage holes for users at the cell edge when using data traffic services. Therefore, in this embodiment, the evaluation of the network device not only involves the evaluation indexes related to VoLTE quality, but also involves the evaluation indexes affecting the quality of data traffic services. And the evaluation scope of the network device is not limited to the adjustment cell itself, but involves the neighboring cells of the adjustment cell or the strong neighboring cells of the adjustment cell. The following combines Figure 5 The flowchart shown to introduce the process of the network device evaluating the adjustment effect of the adjustment cell provided in this embodiment:

[0084] S502: Determine the evaluation area corresponding to the adjustment cell.

[0085] In this embodiment, the evaluation area of an adjustment cell not only includes itself, but also includes its neighboring cells. In one example, the network device can refer toFigure 6 Use the flowchart shown to determine the evaluation area of the adjusted cell:

[0086] S602: Determine the neighboring cells of the adjusted cell based on the MR data of the adjusted cell.

[0087] Among the MR reports sent by users in a cell, there will be information about the neighboring cells of this cell. The neighboring cells measured by users in different positions may be different, and there are many users in a cell. Therefore, through the MR data reported by these numerous users, all the neighboring cells of the cell can be basically determined. For an adjusted cell, the network device can obtain the MR data reported by each user in this cell, and then determine the neighboring cells of this adjusted cell based on the MR data.

[0088] S604: Count the number of times each neighboring cell appears in the MR data of the adjusted cell.

[0089] The network device will also determine the number of times each neighboring cell appears in the MR data according to the MR data of the adjusted cell. Of course, in some examples of this embodiment, the network device can also determine the proportion of each neighboring cell appearing in the MR data of the adjusted cell. However, it should be understood that the amount of MR data obtained by the network device is certain. Therefore, whether it is to determine the number of times each neighboring cell appears in the MR data or the proportion of appearance, the effects are the same.

[0090] S606: Select the top m cells with a higher number of appearances as strong neighboring cells to jointly form the evaluation area of the adjusted cell with the adjusted cell.

[0091] After determining the proportion or the number of times each neighboring cell appears in the MR data, the network device selects the neighboring cells with a relatively high frequency of appearance from these neighboring cells, that is, the strong neighboring cells, to jointly form the evaluation area for evaluating this adjusted cell with the adjusted cell. For example, in one example of this embodiment, the network device can select the top m cells with a higher number of appearances among the neighboring cells to form the evaluation area together with the adjusted cell. In some other examples of this embodiment, the network device can select the top q% with a relatively high proportion among these neighboring cells to form the evaluation area together with the adjusted cell.

[0092] S504: Determine the evaluation indicators of the evaluation area.

[0093] After determining the evaluation area, the network device will calculate the current evaluation indicators of the evaluation area. It can be understood that the current evaluation indicators of the evaluation area represent the VoLTE quality and data flow service quality of the evaluation area after the adjusted cell is adjusted. These qualities need to be compared with the qualities before the adjustment. Therefore, before adjusting the adjusted cell, the network device should determine the corresponding evaluation indicators of this evaluation area.

[0094] In some examples of this embodiment, the evaluation index includes an index characterizing the RSRP of the evaluation area, an index characterizing the CQI of the evaluation area, and an index characterizing the MOS value of the evaluation area.

[0095] The RSRP metrics for the evaluation area include the regional RSRP mean and / or the proportion of cells within the evaluation area with RSRP values < -110. The regional RSRP mean refers to the ratio of the sum of the RSRP values of all cells in the evaluation area to the total number of cells in the evaluation area. For example, assuming there are three cells in the evaluation area, a, b, and c, with RSRP values of x1, x2, and x3, respectively, the regional RSRP mean for the evaluation area is (x1+x2+x3) / 3.

[0096] Indicators representing the MOS value of the assessment area include at least one of the regional MOS mean and the regional quality-poor ratio. The regional MOS mean, similar to the regional RSRP mean, refers to the ratio of the sum of the MOS values of each cell in the assessment area to the total number of cells in the assessment area. For example, for an assessment area consisting of three cells a, b, and c, if the MOS values of these three cells are y1, y2, and y3, respectively, the regional MOS mean of the assessment area is (y1+y2+y3) / 3. The so-called regional quality-poor ratio refers to the ratio of the number of cells in the assessment area with MOS values below a preset threshold to the total number of cells in the assessment area.

[0097] In some examples of this embodiment, the evaluation indicators also include at least one of an indicator characterizing the RSRQ (Reference Signal Received Quality) of the evaluation area, an indicator characterizing the downlink throughput of the evaluation area, an indicator characterizing the PRB (Physical Resource Block) utilization of the evaluation area, and an indicator characterizing the number of activated users in the evaluation area.

[0098] S506: Calculate the first evaluation score before adjustment and the second evaluation score after adjustment of the evaluation area respectively.

[0099] In this embodiment, the network device can be configured according to the formula res=∑ n w i *v i To calculate the first evaluation score before adjustment and the second evaluation score after adjustment. Where i represents the i-th evaluation index, n is the total number of evaluation indicators, v i Represents the value of the i-th evaluation indicator, w iIt represents the weight corresponding to the i-th evaluation metric. It can be understood that in some cases, the metrics of multiple evaluation metrics may be different, and some evaluation metrics belong to "positive metrics", where the larger the value, the better the communication quality is characterized, while some evaluation metrics may be "negative metrics", where the smaller the value, the better the communication quality is characterized. Therefore, for the convenience of calculation, the network device can normalize the evaluation metrics with inconsistent metrics, uniformly convert negative metrics into positive metrics, or uniformly convert positive metrics into negative metrics, and then calculate the evaluation score.

[0100] S508: Determine the difference between the second evaluation score and the first evaluation score.

[0101] After calculating the second evaluation score and the first evaluation score, the network device calculates the difference between the two. Assuming that through the conversion of the network device, all evaluation metrics are positive metrics, then the final evaluation score should also be the larger the better. Therefore, the network device calculates the difference between the second evaluation score and the first evaluation score.

[0102] S414: Determine whether the adjustment is effective.

[0103] If the judgment result is yes, then execute S416; if the judgment result is no, then execute S418.

[0104] For the case where all evaluation metrics are positive metrics, in some examples of this embodiment, after the network device calculates the difference between the second evaluation score and the first evaluation score, as long as the second evaluation score is greater than the first evaluation score, the network device will determine that this adjustment is effective. However, in some other examples of this embodiment, if the network device needs to determine that the difference between the second evaluation score and the first evaluation score reaches a certain threshold to determine that this adjustment is effective, otherwise, even if the second evaluation score is greater than the first evaluation score, the network device will determine that this adjustment is ineffective.

[0105] S416: Delete the adjustment cell from the cells to be optimized.

[0106] If the network device determines that the adjustment to a certain adjustment cell is effective, it can remove the adjustment cell from the cells to be optimized and adjusted. If the network device determines that the adjustment to a certain adjustment cell is ineffective, it will not remove the adjustment cell from the cells to be optimized and adjusted, so that the adjustment cell can have the opportunity to be adjusted again in other adjustment cycles.

[0107] S418: When a new adjustment cycle arrives, determine whether there are still cells in the cells to be optimized.

[0108] If the judgment result is yes, it means that there are still cells in the cells to be optimized waiting to be optimized and adjusted. Therefore, the network device continues to execute S406; otherwise, the process ends.

[0109] In the antenna feeder parameter optimization method provided in this embodiment, after adjusting an adjustment cell, not only the adjustment effect will be evaluated, so as to remedy the adjustment effect of the adjustment cell with poor adjustment effect in time. At the same time, when evaluating the adjustment effect, in order to avoid the problem that the signal of cell-edge users is poor or even there is a coverage hole when using data traffic services due to only considering the VoLTE effect, in this embodiment, when evaluating, the evaluation scope will be expanded, and the evaluation indicators in terms of data traffic services will be taken into account at the same time, so as to ensure the signal strength of data traffic services while improving the VoLTE quality.

[0110] Embodiment Three:

[0111] This embodiment provides an antenna feeder parameter optimization device. Please refer to Figure 7 the structural schematic diagram of the antenna feeder parameter optimization device 70 shown:

[0112] The antenna feeder parameter optimization device 70 includes a problem location module 702, a cell selection module 704, a parameter optimization module 706, and an effect evaluation module 708. Among them, the problem location module 702 is used to determine that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons; the cell selection module 704 is used to determine the adjustment cell for this optimization from the cells to be optimized, and the adjustment cell and other adjustment cells in this adjustment period are not in a strong neighbor relationship; the parameter optimization module 706 is used to determine the target antenna feeder parameters of the adjustment cell, and adjust the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters; the effect evaluation module 708 is used to evaluate the effect of this adjustment. If the evaluation result indicates that the adjustment of the adjustment cell is effective, the adjustment of the adjustment cell will be maintained.

[0113] The problem location module 702 determines whether the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons. If the judgment result is yes, that is, the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, the antenna feeder parameters can be optimized by the cell selection module 704 and the parameter optimization module 706. Otherwise, it is not suitable to use the antenna feeder parameter optimization solution provided in this embodiment to solve the problem.

[0114] The so-called cell to be optimized refers to a cell with poor current VoLTE quality that needs to be optimized. Usually, there can be multiple cells to be optimized, but this is not a limitation, but because in some cases, there can be only one cell to be optimized.

[0115] In some examples of this embodiment, the cell to be optimized is automatically selected by the antenna feed parameter optimization device 70. For example, in some examples, the antenna feed parameter optimization device 70 can evaluate the VoLTE quality of each cell in a predetermined area, so as to select the cells with poor voice service quality as the cells to be optimized. For example, Figure 8 The provided antenna feed parameter optimization device 70 further includes a problem discovery module 700. The problem discovery module 700 can determine the MOS value of each cell in the predetermined area, and then determine the cell to be optimized according to the MOS value of each cell. In one example, the problem discovery module 700 can obtain the VoLTE data and MR data of all cells in the predetermined area, then associate the VoLTE data and MR data of a cell, and calculate the MOS value at the slice level (5 seconds as a slice). After calculating the MOS of each cell, the problem discovery module 700 determines whether the MOS value of each slice is of poor quality, and respectively determines the MOS poor quality ratio of each cell (that is, the number of slice MOS values of poor quality in the cell / the number of all slice MOS values in the cell), and determines the cells with the top 1 / 3 MOS poor quality ratio as the cells to be optimized. Assume that there are six cells a, b, c, d, e, f in the predetermined area. When the problem discovery module 700 selects the cell to be optimized, it will select 1 / 3 of the cells from these six cells, that is, two cells. If the cells with the first and second MOS poor quality ratios among these six cells are c and a respectively, then the cells to be optimized determined by the problem discovery module 700 are a and c.

[0116] In some other examples of this embodiment, the cell to be optimized can be reported manually by other devices or network management personnel. Because when the VoLTE quality of a certain cell is poor, the voice service of the users in that cell will surely be affected and can be perceived by the users. The network management personnel can specify the cell to be optimized to the antenna feed parameter optimization device 70 according to user complaints, etc.

[0117] The so-called preset reasons in this embodiment include but are not limited to coverage reasons, interference reasons, and capacity reasons. If it is determined through the judgment of the problem location module 702 that the reason for the poor VoLTE quality of the cell to be optimized is one of the above three reasons, then the optimization of the VoLTE quality of the cell to be optimized can be achieved through this case. It can be understood that the reasons for the poor VoLTE quality of not all cells to be optimized are the same. For example, in one example of this embodiment, the poor VoLTE quality of the cell to be optimized a is due to coverage reasons, while the cell to be optimized c is due to interference problems.

[0118] After the problem location module 702 determines that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, the cell selection module 704 can select the adjustment cell for this optimization from the cells to be optimized. Generally, within one adjustment period, one or more adjustment cells in the cells to be optimized can be adjusted, and each adjustment cell in the same adjustment period can form an "adjustment cell set". However, in order to avoid adjusting two adjacent cells within the same period, resulting in a poor optimization effect, there is no strong neighbor relationship between the cells belonging to the same adjustment cell set in this embodiment. For a cell, its strong neighbor refers to a neighbor cell with a relatively strong correlation with this cell:

[0119] In some examples of this embodiment, when the cell selection module 704 determines whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, it can be determined based on the MR of this cell, and determine whether the frequency of occurrence of this neighbor cell in the MR of this cell is high enough. If so, the cell selection module 704 determines that this neighbor cell is a strong neighbor cell of this cell; otherwise, it is not a strong neighbor cell. In one example of this embodiment, only when the proportion of a neighbor cell appearing in the MR of a cell reaches the preset strong neighbor cell proportion will this neighbor cell be determined as the strong neighbor cell of the corresponding cell. That is, when the number of MRs of a cell is certain, only after the number of times a neighbor cell appears in it reaches the preset strong neighbor cell threshold will it be recognized as the strong neighbor cell of the corresponding cell. For example, in some examples, the proportion of the strong neighbor cell of a cell a appearing in the MR of a cell a must reach 5%. If the terminals in cell a have reported a total of 100 MRs, then if a cell is a strong neighbor cell of cell a, the number of times this cell appears in the MR of cell a is at least 5 times.

[0120] In some other examples of this embodiment, when the cell selection module 704 determines whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, it can determine the proportion of all neighbor cells of this cell appearing in the MR of this cell, and then select the top k neighbor cells as strong neighbor cells. For example, a cell b has 80 neighbor cells, and 10 of them will be determined as strong neighbor cells. Then, the frequencies or proportions of these 80 neighbor cells appearing in the MR of cell b can be determined respectively, and then the top 10 neighbor cells with higher frequencies or proportions of occurrence are selected as the strong neighbor cells of cell b.

[0121] In some examples of this embodiment, when the cell selection module 704 determines the adjustment cells in this adjustment from the optimized cells, it can first determine the adjustment cell set:

[0122] In some examples of this embodiment, when the cell selection module 704 determines the set of adjustment cells corresponding to the current adjustment period from the cells to be optimized, it only needs to ensure that there is no strong neighbor cell relationship among the cells in the set of adjustment cells. In this case, the cell selection module 704 can first randomly select a cell from the cells to be optimized and add it to the set of adjustment cells. Then, it selects a cell from the remaining cells to be optimized that has no strong neighbor cell relationship with the cells in the set of adjustment cells and adds it to the set of adjustment cells. Subsequently, the cell selection module 704 determines whether there are still cells in the cells to be optimized that have no strong neighbor cell relationship with the cells in the set of adjustment cells. If the determination result is yes, the cell selection module 704 continues to select adjustment cells from the cells to be optimized and add them to the set of adjustment cells. Otherwise, it means that the selection of the set of adjustment cells for this adjustment period has been completed. Therefore, the cell selection module 704 stops selecting.

[0123] Certainly, in some examples of this embodiment, the cell selection module 704 can also limit the selection and determination of the set of adjustment cells by the number. For example, if the number of cells in the set of adjustment cells cannot exceed 6, then when the cell selection module 704 selects adjustment cells in the above manner, although there are still other eligible cells remaining in the cells to be optimized, because the number of cells in the current set of adjustment cells has reached 6, the cell selection module 704 will not continue to select.

[0124] In some examples of this embodiment, not only is it required that there is no strong neighbor cell relationship among the cells selected by the cell selection module 704 in the set of adjustment cells, but also the impact brought about after the selected adjustment cells are adjusted is considered. Generally, if the number of users in a cell is large, then when the VoLTE quality of this cell is poor, it will cause a large number of users to have a bad voice service experience. Therefore, the cell selection module 704 should preferably ensure the VoLTE quality of such cells with a large number of users or a wide coverage area. Therefore, in some examples of this embodiment, when determining the set of adjustment cells, the cell selection module 704 can preferably select the cells with a large number of users from the cells to be optimized. For example, in one example of this embodiment, the cell selection module 704 can select the top n cells with a large reported MR number or the cells with a reported MR number greater than m to form the set of adjustment cells corresponding to the current adjustment period.

[0125] In addition, if the VoLTE quality of a cell is very poor, optimizing such a cell as soon as possible also has a good optimization effect. Therefore, in an example of this embodiment, when the cell selection module 704 determines the adjusted cell set, it preferentially selects cells with a lower average MOS value of the cell. For example, the cell selection module 704 selects cells with an average MOS value lower than the preset average value to form the adjusted cell set corresponding to this adjustment period. The average MOS value of the cell mentioned here refers to the average MOS value determined based on at least two MOS values of a cell.

[0126] Of course, the cell selection module 704 can also combine the above two principles to select cells with a larger coverage area and relatively poor VoLTE quality originally to form the adjusted cell set. For example, in an example of this embodiment, the cell selection module 704 can first select the top n cells with a larger reported MR count or cells with a reported MR count greater than m from each cell to be optimized to form the first cell set, and then select cells with an average MOS value lower than the preset average value from the first cell set to form the adjusted cell set corresponding to this adjustment period. In another example of this embodiment, the cell selection module 704 can first select cells with an average MOS value lower than the preset average value from each cell to be optimized to form the second cell set, and then select the top n cells with a larger reported MR count or cells with a reported MR count greater than m from the second cell set to form the adjusted cell set corresponding to this adjustment period. Those skilled in the art can understand that the cell selection module 704 can also select the first cell set and the second cell set at the same time, and then determine the intersection cells of the first cell set and the second cell set to form the adjusted cell set.

[0127] After determining the adjusted cell set, when the cell selection module 704 determines the adjusted cell from the adjusted cell set, it can randomly select an unadjusted cell from it.

[0128] It can be understood that the parameter optimization module 706 can only adjust the antenna feed parameters of one adjusted cell in each adjustment process. However, because the parameter optimization module 706 needs to interact with the base station in each adjustment process, if only one adjusted cell in the adjusted cell set is adjusted in each adjustment process, when there are multiple adjusted cells in the adjusted cell set, the parameter optimization module 706 needs to interact with the base station multiple times. Therefore, in some examples of this embodiment, the cell selection module 704 can select two or more adjusted cells from the adjusted cell set in each adjustment process. For example, in an example of this embodiment, the parameter optimization module 706 can adjust all the adjusted cells in the adjusted cell set in one adjustment process.

[0129] In the above example, the cell selection module 704 first determines an adjustment cell set from the cells to be optimized, and then selects an adjustment cell for this adjustment from the adjustment cell set. However, in some other examples of this embodiment, the cell selection module 704 can directly select an adjustment cell from the cells to be optimized without first determining the adjustment cell set. In such an example, the cell selection module 704 also needs to ensure that there is no strong neighbor cell relationship among the selected adjustment cells in the same adjustment period. For example, if in a certain adjustment process, the cell selection module 704 selects two cells from the cells to be optimized as adjustment cells, on the one hand, the cell selection module 704 needs to ensure that these two cells and the adjustment cells selected in other adjustment processes in this adjustment period are not in a strong neighbor cell relationship, and on the other hand, the cell selection module 704 also needs to ensure that the two cells selected this time are not in a strong neighbor cell relationship with each other.

[0130] After the cell selection module 704 determines the adjustment cell for this adjustment, the parameter optimization module 706 can determine the target antenna feeder parameters of the adjustment cell. The so-called target antenna feeder parameters are the antenna feeder parameters that can make the VoLTE quality relatively ideal determined according to the relevant performance, attributes, etc. of the adjustment cell. Optionally, the parameter optimization module 706 can determine the target antenna feeder parameters of the adjustment cell according to at least one of the inherent attribute parameters and performance index parameters of the adjustment cell. The inherent attribute parameters mentioned here include but are not limited to the station height of the adjustment cell. The performance index parameters include at least one of the following parameters: RSRP, TA, over-coverage, and weak coverage. Of course, those skilled in the art can understand that the performance index parameters can also be other parameters.

[0131] After determining the target antenna feeder parameters of the adjustment cell, the parameter optimization module 706 can adjust the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters. Usually, the parameter optimization module 706 can send an adjustment instruction to the base station, and the adjustment instruction sent contains the target antenna feeder parameters of the adjustment cell. In this way, when the base station receives the adjustment instruction sent by the parameter optimization module 706, it can adjust the antenna feeder parameters of the corresponding cell according to the target antenna feeder parameters therein.

[0132] It should be understood that before the parameter optimization module 706 adjusts the antenna feeder parameters of the adjustment cell, it should first determine that the current antenna feeder parameters of the adjustment cell are inconsistent with the determined target antenna feeder parameters. Otherwise, if the current antenna feeder parameters of the adjustment cell are consistent with the target antenna feeder parameters, then the parameter optimization module 706 has no need to send an adjustment instruction to the base station.

[0133] After the parameter optimization module 706 adjusts the antenna feed parameters of the adjusted cell, the effect evaluation module 708 will also evaluate the effect of this adjustment. If the evaluation result indicates that this adjustment is effective, the effect evaluation module 708 will maintain this adjustment. The so-called effective adjustment here means that the VoLTE quality of the adjusted cell has improved after the adjustment. In some other examples of this embodiment, the so-called effective adjustment not only requires that the VoLTE quality of the adjusted cell has improved after the adjustment, but also has requirements for the degree of improvement.

[0134] It can be understood that if the effect evaluation module 708 determines that its adjustment to a certain adjusted cell is effective, the parameter optimization module 706 will not perform optimization adjustments on this adjusted cell in this adjustment cycle and subsequent other adjustment cycles. Therefore, the effect evaluation module 708 can delete this adjusted cell from the cells to be optimized.

[0135] If the effect evaluation module 708 determines through evaluation that this adjustment is ineffective, it can consider rolling back this adjustment, that is, restoring the antenna feed parameters of the adjusted cell to the state before the adjustment. In some other examples of this embodiment, the effect evaluation module 708 will not roll back this adjustment. However, in either case, the effect evaluation module 708 will not let the adjusted cell with ineffective adjustment continue to remain in the cells to be optimized so as to continue to optimize this cell in the subsequent process.

[0136] The antenna feed parameter optimization device provided in this embodiment can determine the cells to be optimized according to the MOS value conditions of each cell in a predetermined area, then select the adjusted cells that need to be optimized and adjusted this time from the cells to be optimized, and then determine the target antenna feed parameters of the adjusted cells according to at least one of the inherent attribute parameters and performance index parameters of the adjusted cells. If the antenna feed parameter optimization device determines that the current antenna feed parameters of this adjusted cell are inconsistent with the target antenna feed parameters, it will send an adjustment instruction to the base station to let the base station adjust the antenna feed parameters of the corresponding cell. And after the adjustment, the antenna feed parameter optimization device will also evaluate the evaluation effect and determine whether to maintain the adjustment or roll back the adjustment according to the adjustment effect of the adjusted cell. The antenna feed parameter optimization device can automatically discover problems, analyze problems, locate the causes, solve problems and evaluate the effects on the VoLTE radio side, reducing the manual intervention process and improving the efficiency of wireless network optimization.

[0137] Embodiment 4:

[0138] This embodiment will be based on Figure 7 or Figure 8 to elaborate on the advantages and details of the antenna feed parameter optimization device:

[0139] In this embodiment, the problem discovery module 700 can determine the MOS values of each cell in a predetermined area, and then determine the cells to be optimized based on the MOS values of each cell. For example, the problem discovery module 700 can calculate the slice-level MOS value, and then select the cells with MOS values lower than the preset MOS threshold as the cells to be optimized, or select a certain proportion of cells as the cells to be optimized in ascending order of MOS values. In some examples of this embodiment, the cells to be optimized can also be specified manually.

[0140] It can be understood that when the problem location module 702 in the antenna and feeder parameter optimization device 70 analyzes and locates the reasons for the poor VoLTE quality of the cells to be optimized on the radio side, it does not analyze all the cells in the cells to be optimized as a whole at the same time, but analyzes the cells therein separately. For example, for the cells to be optimized, the reason for the poor VoLTE quality of some cells may be one of the preset reasons, but there may also be some other cells with poor VoLTE quality that are not affected by the preset reasons. In this case, the problems of poor VoLTE quality of some cells to be optimized cannot be solved through the subsequent process results, but the problems of poor VoLTE quality of some cells to be optimized can be solved through the subsequent process.

[0141] Subsequently, the cell selection module 704 can determine the set of adjustment cells corresponding to the current adjustment period from the cells to be optimized. Optionally, when the cell selection module 704 selects each adjustment cell in the current adjustment period from the cells to be optimized, it will not only ensure that the selected adjustment cells are not in a strong neighbor relationship, but also try to select the cells with a relatively larger coverage area and relatively worse VoLTE quality. The process of selecting adjustment cells has been introduced in detail in the foregoing embodiments and will not be elaborated here. In addition, those skilled in the art can understand that when the cell selection module 704 selects the cells constituting the set of adjustment cells, it can also select according to other principles.

[0142] In this embodiment, after the cell selection module 704 selects all the adjustment cells for one adjustment period, the parameter optimization module 706 can adjust these adjustment cells together. That is, for each adjustment cell within the same adjustment period, the parameter optimization module 706 does not determine the target antenna and feeder parameters in batches. Therefore, after determining the set of adjustment cells, the parameter optimization module 706 determines the corresponding target antenna and feeder parameters according to the inherent attribute parameters and performance index parameters of each adjustment cell respectively.

[0143] After the parameter optimization module 706 determines the target antenna feeder parameters corresponding to each adjusted cell, it sends these target antenna feeder parameters to the base station through an adjustment instruction, enabling the base station to obtain the target antenna feeder parameters of each adjusted cell in the adjusted cell set according to this adjustment instruction, and complete the adjustment of these adjusted cells according to the target antenna feeder parameters.

[0144] In some examples of this embodiment, when evaluating the adjustment effect of an adjusted cell, the effect evaluation module 708 can only evaluate each evaluation index that can reflect the VoLTE quality before and after the adjustment of the adjusted cell, and then compare whether the evaluation index after the adjustment is better than the evaluation index before the adjustment, so as to determine whether the current adjustment is effective.

[0145] However, since a cell supporting VoLTE is also carrying data traffic services at the same time, and generally speaking, in terms of coverage, the coverage of VoLTE (QCI is 1 or 2) is smaller than the coverage of LTE data traffic (QCI is 8 or 9). Therefore, if only the VoLTE effect is considered when evaluating the adjustment effect, it may cause problems such as poor signal or even coverage holes for users at the cell edge when using data traffic services. Therefore, in this embodiment, the evaluation of the effect evaluation module 708 not only involves the evaluation indexes related to VoLTE quality, but also involves the evaluation indexes affecting the quality of data traffic services. Moreover, the evaluation scope of the effect evaluation module 708 is not limited to the adjusted cell itself, but involves the strong neighboring cells of the adjusted cell.

[0146] In this embodiment, the evaluation area of an adjusted cell not only includes itself, but also includes its neighboring cells.

[0147] In one example, the effect evaluation module 708 can determine the neighboring cells of the adjusted cell according to the MR data of the adjusted cell: the MR reported by users in a cell will contain information about the neighboring cells of this cell, and the neighboring cells measured by users in different positions may be different. Since there are many users in a cell, therefore, through the MR data reported by these many users, all the neighboring cells of the cell can be basically determined. For an adjusted cell, the effect evaluation module 708 can obtain the MR data reported by each user in this cell, and then determine the neighboring cells of this adjusted cell based on the MR data.

[0148] Then, the effect evaluation module 708 counts the number of times each neighboring cell appears in the MR data of the adjusted cell. The effect evaluation module 708 also determines the number of times each neighboring cell appears in the MR data according to the MR data of the adjusted cell. Of course, in some examples of this embodiment, the effect evaluation module 708 can also determine the proportion of each neighboring cell appearing in the MR data of the adjusted cell. However, it should be understood that the amount of MR data obtained by the effect evaluation module 708 is fixed. Therefore, whether determining the number of times each neighboring cell appears in the MR data or the proportion of appearance, the effects are the same.

[0149] Subsequently, the effect evaluation module 708 selects the top m cells with higher appearance times and the adjusted cell to jointly form the evaluation area of the adjusted cell.

[0150] After determining the proportion or the number of times each neighboring cell appears in the MR data, the effect evaluation module 708 selects the neighboring cells with higher appearance frequencies from these neighboring cells, that is, selects the strong neighboring cells, and jointly forms the evaluation area for evaluating the adjusted cell with the adjusted cell. For example, in one example of this embodiment, the effect evaluation module 708 can select the top m cells with higher appearance times in the MR and the adjusted cell to form the evaluation area together. In some other examples of this embodiment, the effect evaluation module 708 can select the top q% with higher appearance proportions among these neighboring cells and form the evaluation area together with the adjusted cell.

[0151] After determining the evaluation area, the effect evaluation module 708 calculates each evaluation index of the current evaluation area. It can be understood that each evaluation index of the current evaluation area represents the VoLTE quality and data flow service quality of the evaluation area after the adjustment of the adjusted cell. These qualities need to be compared with the qualities before the adjustment. Therefore, before adjusting the adjusted cell, the effect evaluation module 708 should determine the corresponding evaluation indexes of the evaluation area.

[0152] In some examples of this embodiment, the evaluation indexes include the index representing the RSRP of the evaluation area, the index representing the CQI of the evaluation area, and the index representing the MOS value situation of the evaluation area.

[0153] Among them, the index representing the RSRP of the evaluation area includes the average value of the regional RSRP and / or the proportion of cells in the evaluation area where RSRP < -110. The average value of the regional RSRP mentioned here refers to the ratio of the sum of the RSRP values of each cell in the evaluation area to the number of cells in the evaluation area. For example, assume that there are three cells a, b, and c in the evaluation area, and the RSRP values of these three cells are x1, x2, and x3 respectively. Then the average value of the regional RSRP of the evaluation area is (x1 + x2 + x3) / 3.

[0154] The indicators for characterizing the MOS value of the evaluation area include at least one of the average MOS value of the area and the proportion of poor quality in the area. Among them, the meaning of the average MOS value of the area is similar to the meaning of the average RSRP value of the area, which refers to the ratio of the sum of the MOS values of each cell in the evaluation area to the number of cells in the evaluation area. For example, for an evaluation area composed of three cells a, b, and c, if the MOS values of these three cells are y1, y2, and y3 respectively, then the average MOS value of the evaluation area is (y1 + y2 + y3) / 3. The so-called proportion of poor quality in the area refers to the ratio of the number of cells with MOS values lower than the preset threshold in the evaluation area to the total number of cells in the evaluation area.

[0155] In some examples of this embodiment, the evaluation indicators further include at least one of the indicators for characterizing the RSRQ of the evaluation area, the indicator for characterizing the downlink throughput rate of the evaluation area, the indicator for characterizing the PRB utilization rate of the evaluation area, and the indicator for characterizing the number of active users in the evaluation area.

[0156] Then, the effect evaluation module 708 calculates the first evaluation score before adjustment and the second evaluation score after adjustment for the evaluation area respectively. In this embodiment, the effect evaluation module 708 can calculate according to the formula res = ∑w i *v i to calculate the first evaluation score before adjustment and the second evaluation score after adjustment. Among them, i represents the i-th evaluation indicator, and v i represents the value of the i-th evaluation indicator, and w i represents the weight corresponding to the i-th evaluation indicator. It can be understood that in some cases, the measurement units of multiple evaluation indicators may be different, and some evaluation indicators belong to "positive indicators", where the larger the value, the better the communication quality, while some evaluation indicators may be "negative indicators", where the smaller the value, the better the communication quality. Therefore, for the convenience of calculation, the effect evaluation module 708 can perform normalization processing on the evaluation indicators with inconsistent measurement units, uniformly convert negative indicators into positive indicators, or uniformly convert positive indicators into negative indicators, and then calculate the evaluation score.

[0157] After calculating the second evaluation score and the first evaluation score, the effect evaluation module 708 calculates the difference between the two. Assuming that through the conversion of the effect evaluation module 708, all evaluation indicators are positive indicators, the final evaluation score should also be the larger the better. Therefore, the effect evaluation module 708 calculates the difference between the second evaluation score and the first evaluation score. Then, the effect evaluation module 708 determines whether the adjustment is effective. If the judgment result is yes, the effect evaluation module 708 deletes the adjusted cell from the cells to be optimized. If the judgment result is no, when the new adjustment cycle arrives, the effect evaluation module 708 determines whether there are still cells in the cells to be optimized. If there are, continue to select the adjusted cell set for adjustment.

[0158] For the case where all evaluation metrics are positive metrics, in some examples of this embodiment, after the effect evaluation module 708 calculates the difference between the second evaluation score and the first evaluation score, as long as the second evaluation score is greater than the first evaluation score, the effect evaluation module 708 will determine that this adjustment is effective. However, in some other examples of this embodiment, if the effect evaluation module 708 needs to determine that the difference between the second evaluation score and the first evaluation score reaches a certain threshold to determine that this adjustment is effective, otherwise, even if the second evaluation score is greater than the first evaluation score, the network device will determine that this adjustment is ineffective.

[0159] If the effect evaluation module 708 determines that the adjustment to an adjustment cell is effective, it can remove the adjustment cell from the cells to be optimized and adjusted. If the effect evaluation module 708 determines that the adjustment to a certain adjustment cell is ineffective, it will not remove the adjustment cell from the cells to be optimized and adjusted, so that the adjustment cell can have the opportunity to be adjusted again in other adjustment cycles.

[0160] If the judgment result is yes, it means that there are still cells to be optimized and adjusted in the cells to be optimized. Therefore, the effect evaluation module 708 continues to select an adjustment cell set for adjustment, otherwise the process ends.

[0161] In this embodiment, the antenna feed parameter optimization device 70 can be deployed on a network device, for example, deployed on a server. The functions of the problem discovery module 700, the problem location module 702, the cell selection module 704, the parameter optimization module 706, and the effect evaluation module 708 can all be implemented by the processor of the network device.

[0162] In the antenna feed parameter optimization device provided in this embodiment, after adjusting an adjustment cell, it will not only evaluate the adjustment effect, so as to promptly remedy the adjustment effect of the adjustment cell with poor adjustment effect. At the same time, when evaluating the adjustment effect, in order to avoid the problem that the signal of cell edge users is poor or even there is a coverage hole when using data traffic services due to only considering the VoLTE effect, in this embodiment, when evaluating, the evaluation scope will be expanded, and the evaluation metrics in terms of data traffic services will be taken into account at the same time, ensuring that while improving the VoLTE quality, the signal strength of data traffic services is guaranteed.

[0163] Embodiment Five:

[0164] This embodiment provides a storage medium, in which one or more computer programs that can be read, compiled, and executed by one or more processors can be stored. In this embodiment, the storage medium can store an antenna feed parameter optimization program, and the antenna feed parameter optimization program can be executed by one or more processors to implement the process of any antenna feed parameter optimization method introduced in the foregoing embodiments.

[0165] In addition, this embodiment provides a network device, such as Figure 9 shown: The network device 90 includes a processor 91, a memory 92, and a communication bus 93 for connecting the processor 91 and the memory 92. The memory 92 may be the aforementioned storage medium storing the antenna feeder parameter optimization program. The processor 91 can read the antenna feeder parameter optimization program, compile it, and execute the process of implementing the antenna feeder parameter optimization method introduced in the foregoing embodiments:

[0166] The processor 91 determines that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons; then determines the adjustment cell for this optimization from the cells to be optimized, and makes the adjustment cell and other adjustment cells in this adjustment period have a non-strong neighbor relationship; subsequently, the processor 91 determines the target antenna feeder parameters of the adjustment cell, and adjusts the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters; the processor 91 evaluates the effect of this adjustment. If the evaluation result indicates that the adjustment of the adjustment cell is effective, the adjustment of the adjustment cell is maintained.

[0167] For a cell, its strong neighbor is a neighbor cell with a relatively strong correlation with this cell:

[0168] In some examples of this embodiment, when the processor 91 determines whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, it can be determined based on the MR of this cell, and determine whether the frequency of occurrence of this neighbor cell in the MR of this cell is high enough. If so, the processor 91 determines that this neighbor cell is a strong neighbor cell of this cell, otherwise, it is not its strong neighbor cell. For example, in one example of this embodiment, only when the proportion of a neighbor cell appearing in the MR of a cell reaches the preset strong neighbor cell proportion, will this neighbor cell be determined as the strong neighbor cell of the corresponding cell. That is, when the number of MRs of a cell is certain, only when the number of times a neighbor cell appears in it reaches the preset strong neighbor cell threshold, will it be recognized as the strong neighbor cell of the corresponding cell. For example, in some examples, the proportion of the strong neighbor cell of a cell a appearing in the MR of a cell a must reach 5%. If the terminals in cell a have reported a total of 100 MRs, then if a cell is a strong neighbor cell of this cell a, the number of times this cell appears in the MR of cell a is at least 5 times.

[0169] In other examples of this embodiment, when the processor 91 determines whether a neighbor cell of a certain cell is a strong neighbor cell of this cell, it can determine the proportion of all neighbor cells of this cell appearing in the MR of this cell, and then select the top k neighbor cells as strong neighbor cells. For example, a cell b has 80 neighbor cells, and 10 of them will be determined as strong neighbor cells. Then the processor 91 can respectively determine the frequency or proportion of these 80 neighbor cells appearing in the MR of cell b, and then select the top 10 neighbor cells with higher frequency or proportion as the strong neighbor cells of cell b.

[0170] Before determining that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, the processor 91 will first determine the MOS values of each cell in the predetermined area, and then determine the cell to be optimized according to the MOS values of each cell.

[0171] Optionally, when the processor 91 determines the MOS value of each cell in the predetermined area, for any cell in the predetermined area, it obtains the VoLTE data and measurement report MR data of the cell; then, it correlates the VoLTE data and MR data of the cell, and determines the MOS value of the cell according to the VoLTE data and MR data.

[0172] In this embodiment, the preset reasons include coverage reasons, interference reasons, and capacity reasons.

[0173] Optionally, when the processor 91 determines the adjustment cell for this optimization from the cells to be optimized, it can determine the set of adjustment cells to be optimized within this adjustment period from the cells to be optimized, and there are no cells with a strong neighbor relationship with each other in the set of adjustment cells; then, it determines the adjustment cell for this optimization from the set of adjustment cells.

[0174] Optionally, when the processor 91 determines the set of adjustment cells to be optimized within this adjustment period from the cells to be optimized, it can determine the set of adjustment cells to be optimized within this adjustment period according to at least one of the coverage range and VoLTE quality of each cell in the cells to be optimized.

[0175] Optionally, the method for the processor 91 to determine the set of adjustment cells to be optimized within this adjustment period according to at least one of the coverage range and VoLTE quality of each cell in the cells to be optimized includes any one of the following two methods:

[0176] Method 1:

[0177] Select the top n cells with a larger reported MR number or cells with a reported MR number greater than m from each cell to be optimized to form the first cell set;

[0178] Select the cells with a cell MOS average value lower than the preset average value from the first cell set to form the set of adjustment cells corresponding to this adjustment period, and the cell MOS average value is the MOS average value determined according to at least two MOS values of a cell;

[0179] Method 2:

[0180] Select the cells with a cell MOS average value lower than the preset average value from each cell to be optimized to form the second cell set, and the cell MOS average value is the MOS average value determined according to at least two MOS values of a cell;

[0181] Select the top n cells with a larger reported MR count or cells with a reported MR count greater than m from the second cell set to form the adjustment cell set corresponding to the current adjustment period.

[0182] If the evaluation result of the processor 91 indicates that the adjustment of the adjustment cell is ineffective, then roll back the adjustment of the adjustment cell.

[0183] When the processor 91 determines the target antenna feeder parameters of the adjustment cell, it can determine the target antenna feeder parameters of the adjustment cell according to the inherent attribute parameters of the adjustment cell and / or the performance index parameters of the adjustment cell.

[0184] The above-mentioned inherent attribute parameters include: the station height of the adjustment cell; the performance index parameters include at least one of the following several parameters of the adjustment cell: reference signal received power RSRP, timing advance TA, over-coverage, and weak coverage.

[0185] It can be understood that when the processor 91 evaluates the effect of this adjustment, it can first determine the evaluation area corresponding to the adjustment cell; then determine the evaluation index of the evaluation area. The evaluation index includes an index representing the RSRP of the evaluation area, an index representing the channel quality indicator CQI of the evaluation area, and an index representing the MOS value situation of the evaluation area. Subsequently, according to the formula res = ∑w i *v i Calculate the first evaluation score before the adjustment and the second evaluation score after the adjustment of the evaluation area respectively, and determine whether this adjustment is effective according to the difference between the second evaluation score and the first evaluation score. res is the evaluation score of the evaluation area, and v i is the value of the i-th evaluation index, and w i is the weight corresponding to the i-th index.

[0186] When the processor 91 determines the evaluation area corresponding to the adjustment cell, it can first determine the neighboring cells of the adjustment cell according to the MR data of the adjustment cell, then count the number of times each neighboring cell appears in the MR data of the adjustment cell, and then select the top m cells with a higher number of appearances, that is, the strong neighboring cells, to jointly form the evaluation area of the adjustment cell with the adjustment cell.

[0187] In some examples of this embodiment, the index representing the MOS value situation of the evaluation area includes at least one of the regional MOS mean value and the regional quality difference ratio. The regional MOS mean value is the ratio of the sum of the MOS values of each cell in the evaluation area to the number of cells in the evaluation area, and the regional quality difference ratio is the ratio of the number of cells with a MOS value lower than the preset threshold in the evaluation area to the total number of cells in the evaluation area.

[0188] In some examples of this embodiment, the metrics for characterizing the RSRP of the evaluation area include the average value of the area RSRP and / or the proportion of cells in the evaluation area where the RSRP < -110. The average value of the area RSRP is the ratio of the sum of the RSRP values of each cell in the evaluation area to the number of cells in the evaluation area.

[0189] In some other examples of this embodiment, the evaluation metrics further include at least one of the metrics for characterizing the reference signal reception quality (RSRQ) of the evaluation area, the metrics for characterizing the downlink throughput rate of the evaluation area, the metrics for characterizing the physical resource block (PRB) utilization rate of the evaluation area, and the metrics for characterizing the number of active users in the evaluation area.

[0190] The network device provided in this embodiment can automatically discover problems, locate them, solve problems, and evaluate the effects, thereby improving the problem-solving efficiency and reducing the dependence on and requirements for human resources. After adjusting an adjusted cell, not only will the adjustment effect be evaluated, so as to promptly remedy the adjustment effect of the adjusted cell with poor adjustment effect. At the same time, when evaluating the adjustment effect, in order to avoid the problem that the signal of cell-edge users is poor or even there is a coverage hole when using data traffic services due to only considering the effect of VoLTE, in this embodiment, when evaluating, the evaluation scope will be expanded, and the evaluation metrics in terms of data traffic services will be taken into account at the same time to ensure that while improving the VoLTE quality, the signal strength of data traffic services is guaranteed.

[0191] Embodiment Six:

[0192] This embodiment will continue to illustrate the antenna feed parameter optimization scheme proposed above in combination with some examples:

[0193] Example 1:

[0194] It is assumed that the network device is used to monitor and optimize the VoLTE quality of each cell in a predetermined area:

[0195] Step 1, the network device obtains the VoLTE data and MR data of all cells in the predetermined area, then correlates the VoLTE data and MR data, and calculates the MOS value at the slice level (one slice is 5 seconds). Subsequently, the network device determines whether the MOS value of each slice is of poor quality, and respectively determines the MOS poor-quality ratio of each cell, and then sorts the MOS poor-quality ratios of all cells in the predetermined area, and determines the top 1 / 3 ranked cells as the cells to be optimized. Suppose cells a, b, and c are determined to be the cells to be optimized.

[0196] Step 2, the network device locates the problems of the cells to be optimized. It can use historical fingerprint data to determine the reasons for the poor VoLTE quality of these cells to be optimized. For example, the network device detects that cells a and b have coverage problems, and cell c has interference problems.

[0197] Step 3: The network device selects each adjustment cell in the current adjustment cycle from the cells to be optimized. The network device not only ensures that the selected adjustment cells are not in a strong neighboring cell relationship with each other, but also tries to select cells with relatively larger coverage areas and relatively worse VoLTE quality.

[0198] Step 4: The network device calculates the optimal antenna azimuth and target antenna parameters for each adjustment cell in the adjustment cell set respectively. If the optimal antenna azimuth is the same as the current angle of the adjustment cell, the network device calculates the next one; if not, the network device adjusts the antenna angle of the adjustment cell to the optimal antenna azimuth.

[0199] Step 5: The network device calculates the evaluation area of the adjustment cell. The network device filters out all neighboring cells of the cell from the MR data of the adjustment cell, and counts the proportion of each neighboring cell appearing in the MR of the adjustment cell, and then takes the set of strong neighboring cells and the adjustment cell as the evaluation area. In this embodiment, the number of times each neighboring cell appears in the MR of the adjustment cell is sorted in descending order, and the top 2 / 3 of the neighboring cells are the strong neighboring cells of the adjustment cell.

[0200] Step 6: The network device calculates the MOS value at the cell slice level in the evaluation area, calculates the average MOS value of the evaluation area through the average MOS value at the slice level, and counts the MOS poor quality ratio of the evaluation area by determining whether each MOS slice is of poor quality, as shown in the following formula:

[0201]

[0202]

[0203] MOS value is the average MOS value of the area, where MOS value is the MOS value of each slice, Num all is the number of MOS slices in the evaluation area, MOS ratio is the poor quality ratio of the area, Num bad is the number of poor quality slices.

[0204] In addition, the network device also obtains the MR data of all cells in the evaluation area, and calculates the average RSRQ value, average CQI value, average RSRP value and the proportion of RSRP < -110 in the evaluation area through a process similar to the above process.

[0205] Step 7: The network device calculates the evaluation score of the evaluation area according to the following formula, including the first evaluation score before adjustment and the second evaluation score after adjustment:

[0206]

[0207] Among them, i represents the i-th evaluation index, n is the total number of evaluation indexes, v i represents the value of the i-th evaluation index, and w i represents the weight corresponding to the i-th evaluation index.

[0208] Step 8: The network device determines the change effect of the evaluation index due to the adjustment of the cell in the evaluation area. If the effect becomes better, the corresponding adjusted cell is deleted from the set of cells to be optimized; if the effect becomes worse, the optimal antenna tilt angle sent is rolled back.

[0209] Example 2:

[0210] It is assumed that the network device is used to monitor and optimize the VoLTE quality of each cell in a predetermined area:

[0211] Step 1: The network device obtains the VoLTE data and MR data of all cells in the predetermined area, then correlates the VoLTE data and MR data, and calculates the MOS value at the slice level (5 seconds as a slice). Subsequently, the network device determines whether the MOS value of each slice is of poor quality, and respectively determines the MOS poor quality ratio of each cell. Then, the MOS poor quality ratios of all cells in the predetermined area are sorted, and the top 1 / 3 of the ranked cells are determined as the cells to be optimized. Assume that cells a, b, and c are determined as the cells to be optimized.

[0212] Step 2: The network device locates the problems of the cells to be optimized. It can use historical fingerprint data to determine the reasons for the poor VoLTE quality of these cells to be optimized. For example, the network device detects that cells a and b have coverage problems, and cell c has interference problems.

[0213] Step 3: The network device selects each adjusted cell in this adjustment period from the cells to be optimized. The network device not only ensures that the selected adjusted cells are not in a strong neighboring cell relationship, but also tries to select cells with a relatively large coverage area and relatively worse VoLTE quality.

[0214] Step 4: The network device calculates the optimal antenna tilt angle and the set of target antenna parameters of each adjusted cell in the adjusted cell set. If the optimal antenna tilt angle is the same as the current angle of the adjusted cell, the network device calculates the next one; if not, the network device adjusts the antenna tilt angle of the adjusted cell to the optimal antenna tilt angle.

[0215] Step 5: The network device calculates the evaluation area of the adjusted cell. The network device filters out all neighboring cells of the adjusted cell from the MR data of the adjusted cell, and counts the proportion of each neighboring cell appearing in the MR of the adjusted cell. Then, the set of the top 2 / 3 of the ranked neighboring cells and the adjusted cell is used as the evaluation area.

[0216] Step 6: The network device calculates the MOS value at the cell slice level in the evaluation area, statistically calculates the average MOS value of the evaluation area through the slice-level MOS value, and statistically calculates the MOS poor-quality ratio of the evaluation area by determining whether each MOS slice is of poor quality, as shown in the following formula:

[0217]

[0218]

[0219] MOS value is the average MOS value of the area, where MOS value is the MOS value of each slice, Num all is the number of MOS slices in the evaluation area, MOS ratio is the poor-quality ratio of the area, Num bad is the number of poor-quality slices.

[0220] In addition, the network device also obtains the MR data of all cells in the evaluation area, and calculates the average RSRQ value, average CQI value, average RSRP value, and the ratio of RSRP < -110 in the evaluation area through a process similar to the above process.

[0221] Step 7: The network device can calculate the downlink throughput, PRB utilization rate, and the number of active users in each evaluation area.

[0222] Step 8: The network device calculates the evaluation scores of the evaluation area according to the following formula, including the first evaluation score before adjustment and the second evaluation score after adjustment:

[0223] Number of active users > thr

[0224] Number of active users ≤ thr

[0225] Among them, thr is a parameter for determining the current load of the cell according to the adjusted cell geographical location and service capabilities, w i , w j are the weights of the selected variables in different cases of the number of active users respectively, i represents the i-th evaluation index, n is the total number of evaluation indexes, and v i represents the value of the i-th evaluation index.

[0226] In some examples of this embodiment, the network device can set the weight values of RSRQ and CQI to be greater than the weight value of RSRP, and evaluate with the goal of focusing on improving the channel quality.

[0227] Step 9: The network device determines the change effect of the evaluation index of the adjusted cell in the adjusted evaluation area. If the effect becomes better, the adjusted cell is deleted from the set of cells to be optimized; if the effect becomes worse, the optimal antenna angle sent is rolled back.

[0228] Obviously, those skilled in the art should understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software (which can be realized by program codes executable by a computing device), firmware, hardware, and their appropriate combinations. In the hardware implementation, the division of the functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component can have multiple functions, or a function or step can be executed by several physical components in cooperation. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium and executed by a computing device, and in some cases, the steps shown or described can be executed in a different order than here. The computer-readable medium can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile discs (DVDs), or other optical disc storage, magnetic cartridges, tapes, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0229] The above content is a further detailed description of the embodiments of the present invention in combination with specific implementation manners, and it cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. An antenna feeder parameter optimization method, comprising: determining that the reason for the poor quality of Voice over LTE (VoLTE) service based on the IP Multimedia Subsystem in the cell to be optimized is one of the preset reasons; determining the adjustment cell for this optimization from the cells to be optimized, where the adjustment cell and other adjustment cells within this adjustment period are not in a strong neighboring cell relationship; determining the target antenna feeder parameters of the adjustment cell, and adjusting the antenna feeder parameters of the adjustment cell according to the target antenna feeder parameters; Evaluate the effect of this adjustment. If the evaluation result indicates that the adjustment to the adjusted cell is effective, then maintain the adjustment to the adjusted cell. Among them, evaluating the effect of this adjustment includes: determining the evaluation area corresponding to the adjusted cell and the evaluation index of the evaluation area; according to the formula res = ∑ n w i *v i Calculate the first evaluation score before the adjustment and the second evaluation score after the adjustment of the evaluation area respectively. The res is the evaluation score of the evaluation area, n is the total number of evaluation indexes, the vi is the value of the i-th evaluation index, and the wi is the weight corresponding to the i-th evaluation index; determine whether this adjustment is effective according to the difference between the second evaluation score and the first evaluation score.

2. The antenna feed parameter optimization method according to claim 1, characterized in that before determining that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons, further comprising: determining the Mean Opinion Score (MOS) value of each cell in a predetermined area; determining the cell to be optimized according to the MOS values of each cell; 3. The antenna feed parameter optimization method according to claim 2, wherein the determining the MOS value of each cell in the predetermined area includes: for any cell in the predetermined area, obtaining the VoLTE data and Measurement Report (MR) data of the cell; correlating the VoLTE data and MR data of the cell; determining the MOS value of the cell according to the VoLTE data and the MR data; 4. The antenna feed parameter optimization method according to claim 1, characterized in that the preset reasons include coverage reason, interference reason, and capacity reason; 5. The antenna feed parameter optimization method according to claim 1, wherein the determining the adjustment cell for this optimization from the cells to be optimized includes: determining the set of adjustment cells to be optimized within this adjustment period from the cells to be optimized, where there are no cells in the set of adjustment cells that are in a strong neighboring cell relationship with each other; determining the adjustment cell for this optimization from the set of adjustment cells; 6. The antenna feed parameter optimization method according to claim 5, characterized in that the determining the set of adjustment cells to be optimized within this adjustment period from the cells to be optimized includes: determining the set of adjustment cells to be optimized within this adjustment period according to at least one of the coverage range and VoLTE quality of each cell in the cells to be optimized; 7. The antenna feed parameter optimization method according to claim 6, wherein, the manner of determining the set of adjustment cells to be optimized within this adjustment period according to at least one of the coverage range and VoLTE quality of each cell in the cells to be optimized includes any one of the following two: Method 1: selecting the top n cells with larger reported MR numbers or cells with reported MR numbers greater than m from each cell to be optimized to form a first cell set; selecting cells with an average MOS value lower than the preset average value from the first cell set to form the set of adjustment cells corresponding to this adjustment period, where the average MOS value of the cell is the average MOS value determined according to at least two MOS values of a cell; Method 2: selecting cells with an average MOS value lower than the preset average value from each cell to be optimized to form a second cell set, where the average MOS value of the cell is the average MOS value determined according to at least two MOS values of a cell; selecting the top n cells with larger reported MR numbers or cells with reported MR numbers greater than m from the second cell set to form the set of adjustment cells corresponding to this adjustment period; 8. The antenna feed parameter optimization method according to claim 1, wherein the evaluating the effect of this adjustment further includes: if the evaluation result indicates that the adjustment of the adjustment cell is ineffective, rolling back the adjustment of the adjustment cell; 9. The antenna feed parameter optimization method according to claim 1, wherein the determining the target antenna feeder parameters of the adjustment cell includes: determining the target antenna feeder parameters of the adjustment cell according to the inherent attribute parameters of the adjustment cell and / or the performance index parameters of the adjustment cell.

10. The antenna feed parameter optimization method according to claim 9, characterized in that, The inherent attribute parameters include: the station height of the adjusted cell; the performance index parameters include at least one of the following several parameters of the adjusted cell: reference signal received power (RSRP), timing advance (TA), over-coverage, and weak coverage.

11. The method for optimizing antenna feeder parameters according to any one of claims 1-10, characterized in that The evaluation indexes include an index characterizing the RSRP of the evaluation area, an index characterizing the channel quality indicator (CQI) of the evaluation area, and an index characterizing the MOS value situation of the evaluation area.

12. The antenna feed parameter optimization method according to claim 11, characterized in that, Determining the evaluation area corresponding to the adjusted cell includes: Determining the neighboring cells of the adjusted cell according to the MR data of the adjusted cell; Counting the number of times each of the neighboring cells appears in the MR data of the adjusted cell; Selecting the top m cells with higher occurrence times as strong neighboring cells to jointly form the evaluation area of the adjusted cell with the adjusted cell.

13. The antenna feed parameter optimization method according to claim 11, characterized in that The index characterizing the MOS value situation of the evaluation area includes at least one of the regional MOS mean value and the regional quality difference ratio. The regional MOS mean value is the ratio of the sum of the MOS values of each cell in the evaluation area to the number of cells in the evaluation area. The regional quality difference ratio is the ratio of the number of cells with MOS values lower than the preset threshold in the evaluation area to the total number of cells in the evaluation area.

14. The antenna feed parameter optimization method according to claim 11, wherein The index characterizing the RSRP of the evaluation area includes the regional RSRP mean value and / or the ratio of the number of cells in the evaluation area with RSRP < -110. The regional RSRP mean value is the ratio of the sum of the RSRP values of each cell in the evaluation area to the number of cells in the evaluation area.

15. The antenna feed parameter optimization method according to claim 11, characterized in that, The evaluation indexes further include at least one of an index characterizing the reference signal receiving quality (RSRQ) of the evaluation area, an index characterizing the downlink throughput rate of the evaluation area, an index characterizing the physical resource block (PRB) utilization rate of the evaluation area, and an index characterizing the number of active users in the evaluation area.

16. An antenna feeder parameter optimization device, comprising: A problem location module, configured to determine that the reason for the poor VoLTE quality of the cell to be optimized is one of the preset reasons; A cell selection module, configured to determine the adjusted cell for this optimization from the cells to be optimized, and the adjusted cell has a non-strong neighboring cell relationship with other adjusted cells in this adjustment period; A parameter optimization module, configured to determine the target antenna feeder parameters of the adjusted cell, and adjust the antenna feeder parameters of the adjusted cell according to the target antenna feeder parameters; An effect evaluation module is used to evaluate the effect of this adjustment. If the evaluation result indicates that the adjustment to the adjusted cell is effective, the adjustment to the adjusted cell is maintained. Among them, the evaluation of the effect of this adjustment includes: determining the evaluation area corresponding to the adjusted cell and the evaluation index of the evaluation area; according to the formula res = ∑ n w i *v i Calculate the first evaluation score before the adjustment and the second evaluation score after the adjustment of the evaluation area respectively. The res is the evaluation score of the evaluation area, n is the total number of evaluation indicators, the vi is the value of the i-th evaluation indicator, and the wi is the weight corresponding to the i-th evaluation indicator; determine whether this adjustment is effective according to the difference between the second evaluation score and the first evaluation score.

17. A network device, the network device includes a processor, a memory, and a communication bus; The communication bus is used to realize the connection and communication between the processor and the memory; The processor is configured to execute one or more programs stored in the memory to implement the steps of the method for optimizing antenna feeder parameters according to any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method for optimizing antenna feeder parameters according to any one of claims 1 to 15.

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