Antenna weight adjustment method and apparatus, and computer readable storage medium

Abstract

The application provides an antenna weight adjustment method, device and computer readable storage medium. The antenna weight adjustment method comprises: obtaining a plurality of MR data under a current scene type, wherein each MR data comprises first RSRP information corresponding to a current antenna weight combination and DOA information corresponding to the current antenna weight combination; traversing all MR data, for each MR data, calculating second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and the DOA information; calculating evaluation information according to all first RSRP information and all second RSRP information; determining an optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations according to the evaluation information; and updating the current antenna weight combination to the optimal antenna weight combination. In the embodiment of the application, the whole processing process does not need human participation, so that the problem of network optimization not being timely can be solved, and the efficiency of network optimization can be improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to, but are not limited to, the field of communication technology, and particularly to an antenna weight adjustment method, apparatus, and computer-readable storage medium. Background Technology

[0002] In the current network architecture, in order to achieve optimal coverage and spectrum efficiency for LTE (Long Term Evolution) or 5G NR (5G New Radio) systems in diverse scenarios, a large amount of human resources is often required for network optimization.

[0003] However, traditional methods of pre-setting antenna weights are no longer sufficient to handle increasingly diverse coverage scenarios. To cover as many scenarios as possible, the number of antenna weight combinations needs to be increased. Therefore, a single cell may have thousands of possible antenna weight combinations. Consequently, for ultra-dense co-frequency networks like LTE or 5G NR, the complexity of the antenna weight search space increases exponentially. This makes traditional manual network optimization methods unable to respond promptly to changes in user activity within the network, thus failing to guarantee optimal coverage and frequency efficiency in manually optimized areas. Therefore, how to achieve faster and more timely antenna weight adjustments to meet network optimization needs is an urgent problem to be solved. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] This invention provides an antenna weight adjustment method, apparatus, and computer-readable storage medium, which can improve the efficiency of network optimization.

[0006] In a first aspect, embodiments of the present invention provide an antenna weighting adjustment method, comprising:

[0007] Acquire multiple Measurement Report (MR) data under the current scene type, wherein each MR data includes the first reference signal receiving power (RSRP) information corresponding to the current antenna weight combination and the direction of arrival (DOA) information corresponding to the current antenna weight combination;

[0008] Traverse all the MR data, and for each MR data, calculate the second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and the DOA information;

[0009] Based on all the first RSRP information and all the second RSRP information, evaluation information corresponding to each antenna weight combination is calculated.

[0010] The optimal antenna weight combination is determined from the current antenna weight combination and all the candidate antenna weight combinations based on all the evaluation information.

[0011] Update the current antenna weight combination to the optimal antenna weight combination.

[0012] Secondly, embodiments of the present invention also provide an antenna weight adjustment device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the antenna weight adjustment method of the first aspect as described above.

[0013] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for performing the antenna weight adjustment method as described above.

[0014] This invention includes the following steps: acquiring multiple measurement report (MR) data under the current scene type, wherein each MR data includes first reference signal received power (RSRP) information corresponding to the current antenna weight combination and direction of arrival (DOA) information corresponding to the current antenna weight combination; traversing all MR data, and for each MR data, calculating second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information; calculating evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information; determining the optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations based on all evaluation information; and updating the current antenna weight combination to the optimal antenna weight combination. According to the solution provided in the embodiments of the present invention, after obtaining MR data under the current scene type, the second RSRP information corresponding to each candidate antenna weight combination is calculated based on the first RSRP information and DOA information in the MR data. Then, the evaluation information corresponding to each antenna weight combination is calculated based on all the first RSRP information and all the second RSRP information. Finally, the optimal antenna weight combination is determined and updated from the current antenna weight combination and all candidate antenna weight combinations based on all the evaluation information. The entire process does not require manual intervention, thus solving the problem that traditional manual network optimization methods cannot respond to changes in users in the network in a timely manner, thereby improving the efficiency of network optimization.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a schematic diagram of a system architecture for performing an antenna weight adjustment method according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart of an antenna weight adjustment method provided in one embodiment of the present invention;

[0019] Figure 3 This is a flowchart illustrating the calculation of the second RSRP information in an antenna weight adjustment method provided in another embodiment of the present invention;

[0020] Figure 4 This is a flowchart illustrating the calculation of evaluation information in an antenna weight adjustment method provided in another embodiment of the present invention;

[0021] Figure 5 This is a flowchart illustrating the calculation of evaluation information in an antenna weight adjustment method provided in another embodiment of the present invention;

[0022] Figure 6 This is a flowchart illustrating the calculation of the second RSRP information in an antenna weight adjustment method provided in another embodiment of the present invention;

[0023] Figure 7 This is a flowchart illustrating the calculation of evaluation information in an antenna weight adjustment method provided in another embodiment of the present invention;

[0024] Figure 8 This is a flowchart of the evaluation information calculated in the antenna weight adjustment method provided in another embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0026] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0027] This invention provides an antenna weight adjustment method, apparatus, and computer-readable storage medium. It acquires MR data for the current scene type and calculates second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information in the MR data. After calculating the second RSRP information corresponding to each candidate antenna weight combination, it calculates evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information. Then, based on all evaluation information, it determines the optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations. Finally, it updates the current antenna weight combination to this optimal antenna weight combination, thereby completing the antenna weight adjustment process for the local area and co-frequency neighboring cells. The entire antenna weight adjustment process does not require manual intervention, solving the problem that traditional manual network optimization methods cannot respond promptly to changes in user activity within the network, thus improving network optimization efficiency.

[0028] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, Figure 1 This is a schematic diagram of a system architecture for performing an antenna weight adjustment method according to an embodiment of the present invention. Figure 1 In the example, the system architecture includes a local base station 100 and multiple neighboring base stations 200. The local base station 100 and all neighboring base stations 200 cooperate to provide signal coverage for the current area. In the current area, the signal coverage range of the local base station 100 forms the local area, and the signal coverage range of the neighboring base stations 200 forms the neighboring area.

[0030] When a terminal newly accesses or switches to any cell in the current area, the corresponding base station can issue a command to the terminal for A3 co-frequency measurement and corresponding location information measurement. Upon receiving the command, the terminal will send MR data to the base station that issued the command. This MR data includes the terminal's RSRP and DOA information in the access cell, as well as the RSRP and DOA information of the terminal's co-frequency neighboring cells in the access cell. The DOA information includes the terminal's horizontal and vertical position information relative to the antenna normal; each RSRP corresponds to one DOA. Those skilled in the art will understand that the DOA information in the current area can be measured based on the terminal's uplink reference signal or channel information, while the DOA information of co-frequency neighboring cells can be measured by transmitting the terminal's uplink reference signal or channel information from the access cell to the co-frequency neighboring cells.

[0031] The system architecture and application scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of system architecture and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0032] It will be understood by those skilled in the art that Figure 1 The system architecture shown does not constitute a limitation on the embodiments of the present invention and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0033] exist Figure 1 In the system architecture shown, both the local base station and the neighboring base station can call their stored antenna weight adjustment program to execute the antenna weight adjustment method.

[0034] Based on the above system architecture, various embodiments of the antenna weight adjustment method of the present invention are proposed.

[0035] like Figure 2 As shown, Figure 2 This is a flowchart of an antenna weight adjustment method provided in an embodiment of the present invention. The antenna weight adjustment method includes, but is not limited to, steps S100, S200, S300, S400 and S500.

[0036] Step S100: Obtain multiple MR data under the current scene type, wherein each MR data includes the first RSRP information corresponding to the current antenna weight combination and the DOA information corresponding to the current antenna weight combination.

[0037] In one embodiment, when the base station sends an instruction to the terminal to perform A3 co-frequency measurement and corresponding location information measurement, all terminals that receive the instruction will report MR data. That is, each terminal will report one MR data. Therefore, the base station can obtain the MR data of all terminals belonging to it, and thus can perform antenna weight adjustment processing based on these MR data in subsequent steps.

[0038] In one embodiment, the current scenario type can have different instances. For example, the current scenario type may be a clustering scenario, a weak coverage scenario, an overlapping coverage scenario, or a tidal effect scenario, depending on the actual scenario situation. This embodiment does not specifically limit this. Furthermore, those skilled in the art will understand that a clustering scenario refers to a scenario where the number of online users is usually low or relatively stable, but during holidays or competition days, users suddenly cluster, causing a sudden increase in the number of online users, but this increase is short-lived; a weak coverage scenario refers to a scenario where, under the current antenna weight configuration, the cell's coverage signal strength is weak; an overlapping coverage scenario refers to a scenario where, under the current antenna weight configuration, the terminal's signal strength in its local area is not significantly different from its signal strength in neighboring cells on the same frequency; and a tidal effect scenario refers to a scenario where, during a specific time period, the number of online users is high, while during another specific time period, the number of online users is low.

[0039] In one embodiment, the MR data reported by each terminal includes first RSRP information corresponding to the current antenna weight combination and DOA information corresponding to the current antenna weight combination. The first RSRP information may include only the terminal's RSRP information in the access cell, or it may include the terminal's RSRP information in the access cell and the RSRP information of the terminal's co-frequency neighboring cells in the access cell, depending on the actual application. This embodiment does not impose a specific limitation on this. Similarly, the DOA information may include only the terminal's DOA information in the access cell, or it may include the terminal's DOA information in the access cell and the DOA information of the terminal's co-frequency neighboring cells in the access cell, depending on the actual application. This embodiment does not impose a specific limitation on this.

[0040] In one embodiment, the current antenna weight combination can be the antenna weights currently configured in this area, or it can include a combination of the antenna weights currently configured in this area and the antenna weights currently configured in neighboring cells at the same frequency. This can be determined according to the actual application, and this embodiment does not impose a specific limitation on it. For example, assuming there are three neighboring cells at the same frequency in this area, the current antenna weight combination can be represented as {W1, W2, W3, W4}, where W1 is the antenna weight currently configured in this area, W2 is the antenna weight currently configured in the first neighboring cell at the same frequency, W3 is the antenna weight currently configured in the second neighboring cell at the same frequency, and W4 is the antenna weight currently configured in the third neighboring cell at the same frequency.

[0041] Step S200: Traverse all MR data, and for each MR data, calculate the second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information.

[0042] In one embodiment, when the base station receives MR data from the terminal, since the first RSRP information and DOA information in the MR data are both corresponding to the current antenna weight combination, the base station is unaware of the RSRP information corresponding to other candidate antenna weight combinations. Therefore, the base station is unsure which antenna weight combination has a better effect in the current scenario. To achieve adaptive adjustment of antenna weights, the base station can traverse all MR data under the current antenna weight combination. For each MR data, the base station calculates the second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information. Therefore, after traversing all MR data, the base station can obtain the RSRP information corresponding to all candidate antenna weight combinations. This allows the base station to determine the optimal antenna weight combination in subsequent steps based on this RSRP information.

[0043] In one embodiment, since the second RSRP information is calculated based on the first RSRP information and DOA information, the second RSRP information corresponds to the first RSRP information. That is, corresponding to the specific content included in the first RSRP information, the second RSRP information may only include the RSRP information of the terminal in the access cell, or it may include the RSRP information of the terminal in the access cell and the RSRP information of the terminal in the co-frequency neighboring cells of the access cell. The only difference is that the antenna weight combination corresponding to the second RSRP information is a candidate antenna weight combination.

[0044] In one embodiment, the candidate antenna weight combination is relative to the current antenna weight combination. For each cell in the current area, the candidate antenna weight combination is a combination of other optional antenna weights besides the current antenna weight combination. Therefore, the candidate antenna weight combination can be other optional antenna weights in this area, or it can include other optional antenna weights in this area and other optional antenna weights in co-frequency neighboring cells. It depends on the actual application situation, and this embodiment does not make a specific limitation. Taking a specific example, assuming that there are 2 co-frequency neighboring cells in this area, and both this area and the co-frequency neighboring cells have 2 other optional antenna weights, then there are a total of 2 candidate antenna weight combinations. One of the candidate antenna weight combinations can be represented as {W 11 W 12 W 13}, while another candidate antenna weight combination can be expressed as {W 21 W22 W 23}, where W 11 W is one of the optional antenna weights for this region. 12 W is one of the optional antenna weights configured for the first co-frequency neighboring cell. 13 W is one of the optional antenna weights configured for the second co-frequency neighboring cell. 21 W is another optional antenna weight for this area. 22 For the antenna weights of the first co-frequency neighboring cell, W is another optional configuration. 23 This is another optional configuration for the antenna weights of the second co-frequency neighboring cell.

[0045] Step S300: Based on all the first RSRP information and all the second RSRP information, calculate the evaluation information corresponding to each antenna weight combination.

[0046] In one embodiment, after the second RSRP information is calculated, the evaluation information corresponding to each antenna weight combination (i.e., including the current antenna weight combination and each candidate antenna weight combination) can be calculated based on all the first RSRP information and all the second RSRP information, so that the base station can determine the optimal antenna weight combination based on these evaluation information in subsequent steps.

[0047] In one embodiment, the evaluation information may have different instances depending on the current scenario type. The specific instance of the evaluation information can be determined according to the actual application situation, and this embodiment does not impose any specific limitations on it. For example, when the current scenario type is a clustered scenario, the evaluation information may be the average signal-to-interference-plus-noise ratio (SINR); when the current scenario type is a weak coverage scenario, the evaluation information may include the weak coverage ratio and the average RSRP; when the current scenario type is an overlapping coverage scenario, the evaluation information may include the overlapping coverage ratio and the average SINR; and when the current scenario type is a tidal effect scenario, the evaluation information may be the average RSRP.

[0048] Step S400: Determine the optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations based on all evaluation information.

[0049] In one embodiment, since the evaluation information corresponds one-to-one with the antenna weight combination, the optimal antenna weight combination can be determined from the current antenna weight combination and all candidate antenna weight combinations based on all the evaluation information. This allows the current antenna weight combination to be updated to the determined optimal antenna weight combination in subsequent steps, thereby adjusting the antenna weights.

[0050] In one embodiment, the optimal antenna weight combination is determined from the current antenna weight combination and all candidate antenna weight combinations based on different instances of evaluation information, which can be implemented in different ways. For example, when the evaluation information is the average SINR, the antenna weight combination corresponding to the highest average SINR is determined as the optimal antenna weight combination; when the evaluation information is the average RSRP, the antenna weight combination corresponding to the highest average RSRP is determined as the optimal antenna weight combination; when the evaluation information includes both the weak coverage ratio and the average RSRP, the antenna weight combination corresponding to the lowest weak coverage ratio is determined as the optimal antenna weight combination; or, if there are multiple antenna weight combinations corresponding to the lowest weak coverage ratio, the antenna weight combination corresponding to the highest average RSRP among these multiple antenna weight combinations is determined as the optimal antenna weight combination; when the evaluation information includes both the overlapping coverage ratio and the average SINR, the antenna weight combination corresponding to the lowest overlapping coverage ratio is determined as the optimal antenna weight combination; or, if there are multiple antenna weight combinations corresponding to the lowest overlapping coverage ratio, the antenna weight combination corresponding to the highest average SINR among these multiple antenna weight combinations is determined as the optimal antenna weight combination.

[0051] Step S500: Update the current antenna weight combination to the optimal antenna weight combination.

[0052] In one embodiment, once the optimal antenna weight combination is determined, the optimal antenna weights for each cell are also determined. At this point, the Channel State Information Reference Signal (CSI-RS) beam weights can be adjusted in conjunction with the optimal antenna weights for each cell. That is, the CSI-RS beam weights are adjusted to the antenna weights corresponding to the optimal antenna weight combination. Then, all the optimized antenna weights are spliced ​​together, and these spliced ​​antenna weights are then sent down to make them effective, thereby realizing the adjustment of the antenna weights.

[0053] In one embodiment, after the optimized antenna weights are issued, to prevent the adjusted antenna weights from having a significant impact on the performance of the current area, a Key Performance Indicator (KPI) evaluation can be performed on each cell. If the KPI evaluation is passed, the weights are updated; if the KPI evaluation is failed, the weights can be rolled back, and the optimal antenna weight combination can be re-determined. The duration of the KPI evaluation can be configured appropriately according to the actual situation, for example, it can be configured to 1 minute or longer.

[0054] In one embodiment, the KPIs to be evaluated may include basic KPIs and performance KPIs. The basic KPIs may include Radio Resource Control (RRC) connection success rate, handover success rate, and call drop rate, etc.; the performance KPIs may include Spectral Efficiency (SE) and average number of active users, etc.

[0055] It is worth noting that the KPI evaluation statistics begin after the antenna weights take effect and continue until the configured duration is reached. Once the KPIs are statistically analyzed, KPI evaluation can be performed. If the fluctuation of the KPI after antenna weight adjustment relative to the KPI before adjustment is within 5%, it is considered normal fluctuation. The current antenna weights are maintained at the adjusted values, and periodic KPI evaluations are performed until the KPI fluctuation exceeds 5%. The duration and number of evaluation periods can be appropriately configured based on actual application conditions; for example, 10 periodic evaluations could be performed, with each evaluation lasting 15 minutes. If the fluctuation of the KPI after antenna weight adjustment relative to the KPI before adjustment exceeds 5%, it is considered performance degradation. In this case, the current antenna weights are reverted to the original configuration values, and steps S100 to S500 are re-executed to re-execute the antenna weight adjustment method until the KPI fluctuation is within 5%.

[0056] In one embodiment, during the periodic KPI evaluation process, if the fluctuation value remains within 5%, it is also possible to continuously monitor whether the total number of online users in the current area has reached the pre-configured user number threshold. If the pre-configured user number threshold is reached, the current antenna weight can be reverted to the original configuration value, and the above steps S100 to S500 can be re-executed to re-execute the antenna weight adjustment method.

[0057] In one embodiment, by employing an antenna weight adjustment method including the steps S100, S200, S300, S400, and S500 described above, after acquiring multiple MR data under the current scene type, second RSRP information corresponding to each candidate antenna weight combination is calculated based on the first RSRP information and DOA information in the MR data. Then, evaluation information corresponding to each antenna weight combination is calculated based on all first RSRP information and all second RSRP information. Next, the optimal antenna weight combination is determined from the current antenna weight combination and all candidate antenna weight combinations based on all evaluation information. Then, the current antenna weight combination is updated to the optimal antenna weight combination, thereby completing the antenna weight adjustment process for the local area and co-frequency neighboring areas. In the entire antenna weight adjustment process, no manual intervention is required, thus solving the problem that traditional manual network optimization methods cannot respond to changes in users in the network in a timely manner, thereby improving the efficiency of network optimization.

[0058] In another embodiment, when the current scene type is a clustered scene or an overlapping coverage scene, the current antenna weight combination may include the current antenna weight of the current area and the current antenna weight of each co-frequency neighboring cell; the first RSRP information may include a first RSRP measurement value corresponding to the current antenna weight of the current area and a second RSRP measurement value corresponding one-to-one with the current antenna weight of each co-frequency neighboring cell; the DOA information may include a first DOA measurement value corresponding to the current antenna weight of the current area and a second DOA measurement value corresponding one-to-one with the current antenna weight of each co-frequency neighboring cell; the candidate antenna weight combination may include candidate antenna weights of the current area and candidate antenna weights of each co-frequency neighboring cell; the second RSRP information may include a third RSRP prediction value corresponding to the candidate antenna weight of the current area and a fourth RSRP measurement value corresponding one-to-one with the candidate antenna weight of each co-frequency neighboring cell. In this case, refer to Figure 3 The step S200, which calculates the second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information, may include, but is not limited to, the following steps:

[0059] Step S210: Calculate the third RSRP predicted value based on the first RSRP measurement value and the first DOA measurement value;

[0060] Step S220: Calculate the fourth RSRP prediction value based on the second RSRP measurement value and the second DOA measurement value.

[0061] In one embodiment, after the base station receives the MR data reported by the terminal, the base station can only know the first RSRP measurement value corresponding to the current antenna weight of the local area and the second RSRP measurement value corresponding to the current antenna weight of each co-frequency neighboring cell. The base station does not know the RSRP value corresponding to the candidate antenna weight of the local area and the RSRP value corresponding to the candidate antenna weight of each co-frequency neighboring cell. Therefore, the base station can calculate the third RSRP prediction value corresponding to the candidate antenna weight of the local area through the first RSRP measurement value and the first DOA measurement value, and calculate the fourth RSRP prediction value corresponding to the candidate antenna weight of each co-frequency neighboring cell through the second RSRP measurement value and the second DOA measurement value. This makes it easier for the base station to determine the optimal antenna weight combination in subsequent steps based on the first RSRP measurement value, the second RSRP measurement value, all third RSRP prediction values, and all fourth RSRP prediction values.

[0062] In one embodiment, the third RSRP prediction value can be calculated according to the following formula:

[0063]

[0064] Among them, RSRP 本区候选 This is the third RSRP prediction value, RSRP 本区当前 This is the first RSRP measurement value. The power difference is obtained based on the first DOA measurement value and the first preset three-dimensional beam power table corresponding to the current antenna weight of this area.

[0065] It is worth noting that the base station can pre-store a first preset three-dimensional beam power table corresponding to the current antenna weights of the local area. This first preset three-dimensional beam power table includes power values ​​corresponding to the antenna weights and DOA values. Therefore, when the base station obtains the first RSRP measurement value and the first DOA measurement value in the MR data, it can first locate the first preset three-dimensional beam power table, then determine the first position corresponding to the first DOA measurement value in the first preset three-dimensional beam power table, and then determine the second position corresponding to the current antenna weights of the local area and the third position corresponding to the candidate antenna weights currently desired for the local area at the first position in the first preset three-dimensional beam power table. At this time, the power value corresponding to the current antenna weights of the local area can be determined in the first preset three-dimensional beam power table based on the second position, and the power value corresponding to the candidate antenna weights currently desired for the local area can be determined based on the third position. Therefore, the difference between these two power values ​​can be used to obtain the desired power value.

[0066] In one embodiment, the fourth RSRP prediction value can be calculated according to the following formula:

[0067]

[0068] Among them, RSRP 邻区候选 This is the fourth RSRP prediction value, RSRP 邻区当前 This is the second RSRP measurement. The power difference is obtained based on the second DOA measurement value and the second preset three-dimensional beam power table corresponding to the current antenna weight of the co-frequency neighboring cell.

[0069] It is worth noting that the base station can pre-store a second preset three-dimensional beam power table corresponding to the current antenna weights of co-frequency neighboring cells. This second preset three-dimensional beam power table includes power values ​​corresponding to the antenna weights and DOA values. Therefore, when the base station obtains the second RSRP measurement value and the second DOA measurement value from the MR data, it can first locate the second preset three-dimensional beam power table, then determine the fourth position corresponding to the second DOA measurement value in the second preset three-dimensional beam power table, and then determine the fifth position corresponding to the current antenna weights of the co-frequency neighboring cells and the sixth position corresponding to the candidate antenna weights currently desired for the co-frequency neighboring cells at the fourth position in the second preset three-dimensional beam power table. At this point, the power value corresponding to the current antenna weights of the co-frequency neighboring cells can be determined in the second preset three-dimensional beam power table based on the fifth position, and the power value corresponding to the candidate antenna weights currently desired for the co-frequency neighboring cells can be determined based on the sixth position. Therefore, the difference between these two power values ​​can be used to obtain the desired power value.

[0070] In another embodiment, when the current scene type is a clustering scene, the evaluation information may include a first evaluation index corresponding to the current antenna weight combination and a second evaluation index corresponding one-to-one with each candidate antenna weight combination. In this case, refer to... Figure 4 Step S300, which calculates the evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information, may include, but is not limited to, the following steps:

[0071] Step S311: Traverse all MR data, and for each MR data, calculate the first statistical value corresponding to the current antenna weight combination based on the first RSRP measurement value and all second RSRP measurement values;

[0072] Step S312: Obtain the first evaluation index based on all the first statistical values;

[0073] Step S313: For each candidate antenna weight combination, traverse all MR data, and for each MR data, calculate the second statistical value corresponding to the current candidate antenna weight combination based on the third RSRP prediction value and all fourth RSRP prediction values. After traversing all MR data, obtain the second evaluation index corresponding to the current candidate antenna weight combination based on all second statistical values.

[0074] In one embodiment, both the first statistical value and the second statistical value can be SINR values, and both can be obtained using the following formula:

[0075]

[0076] Among them, SINR i For the SINR value corresponding to the i-th MR data, and for the current antenna weight combination, the SINR is... i This is the first statistical value; for candidate antenna weight combinations, SINR... i This is the second statistical value; RSRP 本区 Let RSRP be the value of the i-th MR data under the antenna weights in this region. For the current antenna weight combination, RSRP is... 本区 This is the first RSRP measurement value. For candidate antenna weight combinations, RSRP... 本区 This is the third RSRP prediction value; RSRP 邻区总和 Let RSRP be the value of the i-th MR data under the antenna weights in the co-frequency neighboring cells. For the current antenna weight combination, RSRP is... 邻区总和 That is, the sum of all second RSRP measurements. For candidate antenna weight combinations, RSRP is... 邻区总和 That is, the sum of all fourth RSRP measurements; w 噪声 The power of Gaussian white noise, w 噪声 The typical value can be -105dBm.

[0077] In one embodiment, when both the first and second statistical values ​​are SINR values, both the first and second evaluation indices can be the average SINR. After calculating the first statistical value corresponding to each MR data point according to the above formula, the average of all first statistical values ​​can be taken to obtain the first evaluation index. Similarly, for each candidate antenna weight combination, after calculating the second statistical value corresponding to each MR data point according to the above formula, the average of all second statistical values ​​can be taken to obtain the second evaluation index corresponding to the current candidate antenna weight combination. After obtaining the evaluation index corresponding to each antenna weight combination, the optimal antenna weight combination can be determined in subsequent steps based on these evaluation indices.

[0078] In one embodiment, when the current scene type is a gathering scene, the steps prior to step S100 may include, but are not limited to, the following:

[0079] Step 1: Monitor the number of online users in all cells within the current area. If the number of online users in one cell reaches the preset first user threshold, proceed to Step 2; otherwise, continue monitoring.

[0080] Step 2: Start the timed monitor to prevent the task of identifying the current scene type from remaining in the current state and unable to restart;

[0081] Step 3: Monitor whether the total number of online users in all communities within the current area reaches the preset second user threshold. If yes, proceed to step 5; otherwise, proceed to step 4.

[0082] Step 4: Determine if the timer has reached the preset time threshold. If yes, stop the current task and proceed to step 1; otherwise, proceed to step 5.

[0083] Step 5: Monitor whether the total number of online users in the current area has reached a stable level. If yes, proceed to step S100; otherwise, proceed to step 6.

[0084] Step 6: Determine whether the timer has reached the preset time threshold. If yes, stop the current task and execute step 1; otherwise, execute step S100.

[0085] In one embodiment, whether the total number of online users in the current area has reached a stable level in step 5 can be determined by the following steps:

[0086] The number of online users is counted using a preset and configurable duration as the statistical granularity. If the volatility of three consecutive statistical values ​​is less than 5%, the total number of online users in the current region is considered to have reached stability. Otherwise, the total number of online users in the current region is considered not to have reached stability, and further monitoring of changes in the number of online users in the current region is required. The volatility can be calculated as follows:

[0087] First, calculate the average number of RRC connections for three consecutive times;

[0088] Next, find the maximum and minimum values ​​of the RRC connection number for three consecutive times;

[0089] Then, volatility is calculated based on the average, maximum, and minimum values.

[0090] To illustrate with a specific example, suppose the RRC connection numbers for three consecutive times are RRC0, RRC1, and RRC2. Then the average value is: RRCavr = (RRC0 + RRC1 + RRC2) / 3; the maximum value is: maxRRC = max(RRC0 / RRC1 / RRC2); and the minimum value is: minRRC = min(RRC0 / RRC1 / RRC2). Therefore, volatility can be calculated using the formula (maxRRC - minRRC) / RRCavr.

[0091] In another embodiment, when the current scene type is a gathering scene, step S400 may include, but is not limited to, the following steps:

[0092] The optimal antenna weight combination is determined by identifying the antenna weight combination that corresponds to the largest value among the first evaluation index and all second evaluation indices.

[0093] In one embodiment, since both the first evaluation index and the second evaluation index are the average SINR values, and the larger the average SINR value, the better the effect of the corresponding antenna weight combination, after calculating the first evaluation index corresponding to the current antenna weight combination and the second evaluation index corresponding to each candidate antenna weight combination, the antenna weight combination corresponding to the largest value among the first evaluation index and all second evaluation indices can be determined as the optimal antenna weight combination.

[0094] It is worth noting that, since the number of configurable candidate antenna weights for each cell is very large (generally greater than 1000), the number of candidate antenna weight combinations for N cells is at least 1000. N Such a large number of combinations makes it impossible to determine the optimal antenna weight combination through traversal. To improve the efficiency of determining the optimal antenna weight combination, this embodiment can use a particle swarm optimization algorithm to optimize the processing time. For example, using any one of the first evaluation index and all second evaluation indices as the output of the particle swarm optimization algorithm, 100 iterations are performed to find the one with the largest value among the first evaluation index and all second evaluation indices. The antenna weight combination corresponding to this largest evaluation index can then be considered the optimal antenna weight combination.

[0095] In another embodiment, when the current scene type is an overlapping scene, the evaluation information may include a third evaluation index corresponding to each candidate weight combination and a fourth evaluation index corresponding to each candidate weight combination. In this case, refer to... Figure 5Step S300, which calculates the evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information, may include, but is not limited to, the following steps:

[0096] Step S314: Based on the first RSRP measurement value, all second RSRP measurement values, the third RSRP prediction value, all fourth RSRP prediction values, and the first RSRP threshold, determine a set of candidate weights from the current antenna weight combination and all candidate antenna weight combinations. The set of candidate weights includes multiple candidate weight combinations, which include the candidate weights of this area and the candidate weights of each co-frequency neighboring cell.

[0097] Step S315: For each candidate weight combination, traverse all MR data, and for each MR data, calculate the third statistical value corresponding to the current candidate weight combination based on the RSRP value corresponding to the candidate weight value of this region in the current candidate weight combination and the RSRP value corresponding to the candidate weight value of each co-frequency neighboring region in the current candidate weight combination. After traversing all MR data, calculate the third evaluation index corresponding to the current candidate weight combination based on all the third statistical values ​​corresponding to the current candidate weight combination.

[0098] Step S316: For each candidate weight combination, obtain the number of RSRP values ​​that satisfy the overlap coverage condition among all the candidate weight values ​​in the current candidate weight combination and the region corresponding to the candidate weight value in the current candidate weight combination. Based on the number of values ​​that satisfy the overlap coverage condition and the number of RSRP values ​​that satisfy the current candidate weight combination and the region corresponding to the candidate weight value in the current candidate weight combination, calculate the fourth evaluation index corresponding to the current candidate weight combination. The third evaluation index and the fourth evaluation index correspond one-to-one.

[0099] In one embodiment, after acquiring MR data from the terminal, it is first determined which cells belong to overlapping coverage. Then, based on the antenna weights of these overlapping coverage cells, evaluation information corresponding to each antenna weight combination is calculated. Specifically, whether the current cell belongs to overlapping coverage can be determined as follows: traverse all MR data. If there are MR data that meet the overlapping coverage condition, and the proportion of MR data meeting the overlapping coverage condition exceeds 5% of all MR data, then the current cell is considered to belong to overlapping coverage. The overlapping coverage condition is: the difference between the first RSRP measurement value and the largest of all second RSRP measurements is less than 6 dB. It is worth noting that antenna weight optimization can be performed only for cells belonging to overlapping coverage; for cells not belonging to overlapping coverage, antenna weight optimization can be omitted.

[0100] In one embodiment, step S314, which determines the set of candidate antenna weights from the current antenna weight combination and all candidate antenna weight combinations based on the first RSRP measurement value, all second RSRP measurements, the third RSRP prediction value, all fourth RSRP prediction values, and the first RSRP threshold, can specifically be as follows:

[0101] Among the first RSRP measurement, all second RSRP measurements, the third RSRP prediction, and all fourth RSRP predictions, find all RSRP values ​​that are greater than or equal to the first RSRP threshold. The antenna weights corresponding to these RSRP values ​​that are greater than or equal to the first RSRP threshold are the set of candidate weights.

[0102] It is worth noting that the first RSRP threshold can be implemented in different ways. For example, the first RSRP threshold can be a preset threshold or the average RSRP calculated based on the RSRP values ​​corresponding to the current antenna weight and all candidate antenna weights. This embodiment does not specifically limit this. When the first RSRP threshold is the average RSRP calculated based on the RSRP values ​​corresponding to the current antenna weight and all candidate antenna weights, each cell corresponds to a first RSRP threshold. That is, for each cell, the average value is calculated using the current antenna weight and the RSRP values ​​corresponding to all candidate antenna weights of the cell to obtain the first RSRP threshold corresponding to that cell. In addition, after calculating all the first RSRP thresholds, for each cell, the RSRP values ​​corresponding to all antenna weights are compared with the first RSRP thresholds to find the antenna weights corresponding to the cell whose RSRP values ​​are greater than or equal to the first RSRP threshold. Therefore, after traversing all cells, a set of candidate weights can be obtained.

[0103] In one embodiment, the third statistical value can be the SINR value, which can be obtained using the following formula:

[0104]

[0105] Among them, SINR i The SINR value corresponding to the i-th MR data point, i.e., the third statistical value; RSRP 本区 Let RSRP be the RSRP value of the i-th MR data point under the current candidate weight combination and the candidate weight values ​​in this region; RSRP 邻区总和 w is the sum of the RSRP values ​​of the i-th MR data under the current candidate weight combination and the candidate weights of the co-frequency neighboring cells; 噪声 The power of Gaussian white noise, w 噪声 The typical value can be -105dBm.

[0106] In one embodiment, when the third statistic is the SINR value, the third evaluation index can be the average SINR. For each candidate weight combination, after calculating the third statistic corresponding to each MR data according to the above formula, the average of all third statistic values ​​can be taken to obtain the third evaluation index corresponding to the current candidate weight combination.

[0107] In one embodiment, the fourth evaluation index can be the overlap coverage ratio, which can be obtained using the following formula:

[0108]

[0109] Among them, OverCoverRatio 本区 This refers to the weak coverage ratio of the candidate weights in this region under the current candidate weight combination, which is the fourth evaluation index corresponding to the candidate weights in this region under the current candidate weight combination; P 和 P is the number of RSRP values ​​that satisfy the overlap coverage condition among all candidate weights in the current candidate weight combination for this region; 总 This represents the number of all RSRP values ​​corresponding to the candidate weights in this region within the current candidate weight combination.

[0110] In one embodiment, once the third and fourth evaluation indices corresponding to all candidate weight combinations are obtained, the optimal antenna weight combination can be determined in subsequent steps using these third and fourth evaluation indices.

[0111] In another embodiment, when the current scene type is an overlapping scene, step S400 may include, but is not limited to, the following steps:

[0112] The optimal antenna weight combination is determined by identifying the antenna weight combination with the smallest value among all the fourth evaluation indicators.

[0113] In one embodiment, since the fourth evaluation index is the overlap coverage ratio, and the smaller the overlap coverage ratio, the better the effect of the corresponding antenna weight combination, after calculating the fourth evaluation index corresponding to all candidate weight combinations, the antenna weight combination corresponding to the smallest value among all the fourth evaluation indexes can be determined as the optimal antenna weight combination.

[0114] It is worth noting that, since the number of configurable candidate antenna weights for each cell is very large (generally greater than 1000), the number of candidate antenna weight combinations for N cells is at least 1000. NSuch a large number of combinations makes it impossible to determine the optimal antenna weight combination through traversal. To improve the efficiency of determining the optimal antenna weight combination, this embodiment can use a particle swarm optimization algorithm to optimize the processing time. For example, using any one of the fourth evaluation indicators as the output of the particle swarm optimization algorithm, 100 iterations are performed to find the one with the smallest value among all the fourth evaluation indicators. At this point, the antenna weight combination corresponding to the fourth evaluation indicator with the smallest value can be considered the optimal antenna weight combination.

[0115] In another embodiment, when the current scene type is an overlapping scene, step S400 may include, but is not limited to, the following steps:

[0116] When there are two or more fourth evaluation indicators with the smallest values ​​among all the fourth evaluation indicators, the antenna weight combination corresponding to the third evaluation indicator with the largest value among all the third evaluation indicators corresponding to the fourth evaluation indicator with the smallest value is determined as the optimal antenna weight combination.

[0117] It is worth noting that the steps in this embodiment and the step in the above embodiment of determining the antenna weight combination corresponding to the smallest value among all fourth evaluation indicators as the optimal antenna weight combination are parallel technical solutions.

[0118] In one embodiment, since the fourth evaluation index is the overlap coverage ratio and the third evaluation index is the average SINR, the smaller the overlap coverage ratio or the larger the average SINR, the better the effect of the corresponding antenna weight combination. Therefore, when there are more than two fourth evaluation indices with the smallest values, the antenna weight combination corresponding to the largest value among all the third evaluation indices corresponding to the smallest fourth evaluation index can be determined as the optimal antenna weight combination.

[0119] It is worth noting that in this embodiment, the particle swarm optimization algorithm can also be used to optimize and determine the processing time of the largest of all third evaluation indicators corresponding to the smallest fourth evaluation indicator. When the particle swarm optimization algorithm is used to determine the largest of all third evaluation indicators corresponding to the smallest fourth evaluation indicator, the antenna weight combination corresponding to the largest third evaluation indicator can be considered as the optimal antenna weight combination.

[0120] In another embodiment, when the current scenario type is a weak coverage scenario or a tidal effect scenario, the current antenna weight combination includes the current antenna weights of this area; the first RSRP information includes the first RSRP measurement value corresponding to the current antenna weights of this area; the DOA information includes the first DOA measurement value corresponding to the current antenna weights of this area; the candidate antenna weight combination includes the candidate antenna weights of this area; and the second RSRP information includes the third RSRP prediction value corresponding to the candidate antenna weights of this area. In this case, refer to... Figure 6 The step S200, which calculates the second RSRP information corresponding to each candidate antenna weight combination based on the first RSRP information and DOA information, may include, but is not limited to, the following steps:

[0121] Step S230: Calculate the third RSRP predicted value based on the first RSRP measurement value and the first DOA measurement value.

[0122] In one embodiment, when the base station receives MR data reported by the terminal, the base station can only know the first RSRP measurement value corresponding to the current antenna weight of the area. The base station does not know the RSRP value corresponding to the candidate antenna weight of the area. Therefore, the base station can calculate the third RSRP prediction value corresponding to the candidate antenna weight of the area through the first RSRP measurement value and the first DOA measurement value. This makes it easier for the base station to determine the optimal antenna weight combination in subsequent steps based on the first RSRP measurement value and all the third RSRP prediction values.

[0123] In one embodiment, the third RSRP prediction value can be calculated according to the following formula:

[0124]

[0125] Among them, RSRP 本区候选 This is the third RSRP prediction value, RSRP 本区当前 This is the first RSRP measurement value. The power difference is obtained based on the first DOA measurement value and the first preset three-dimensional beam power table corresponding to the current antenna weight of this area.

[0126] It is worth noting that the base station can pre-store a first preset three-dimensional beam power table corresponding to the current antenna weights of the local area. This first preset three-dimensional beam power table includes power values ​​corresponding to the antenna weights and DOA values. Therefore, when the base station obtains the first RSRP measurement value and the first DOA measurement value in the MR data, it can first locate the first preset three-dimensional beam power table, then determine the first position corresponding to the first DOA measurement value in the first preset three-dimensional beam power table, and then determine the second position corresponding to the current antenna weights of the local area and the third position corresponding to the candidate antenna weights currently desired for the local area at the first position in the first preset three-dimensional beam power table. At this time, the power value corresponding to the current antenna weights of the local area can be determined in the first preset three-dimensional beam power table based on the second position, and the power value corresponding to the candidate antenna weights currently desired for the local area can be determined based on the third position. Therefore, the difference between these two power values ​​can be used to obtain the desired power value.

[0127] Additionally, in one embodiment, when the current scenario type is a weak coverage scenario, the evaluation information may include a fifth evaluation index corresponding to the current antenna weight of the current area, a sixth evaluation index corresponding to the current antenna weight of the current area, a seventh evaluation index corresponding to the candidate antenna weight of each current area, and an eighth evaluation index corresponding to the candidate antenna weight of each current area. In this case, refer to Figure 7 Step S300, which calculates the evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information, may include, but is not limited to, the following steps:

[0128] Step S321: Obtain the number of first RSRP measurements whose values ​​are less than a preset weak coverage threshold among all first RSRP measurements, and calculate the fifth evaluation index based on the number of first RSRP measurements whose values ​​are less than the preset weak coverage threshold and the total number of all first RSRP measurements.

[0129] Step S322: Obtain the sum of all first RSRP measurements, and calculate the sixth evaluation index based on the sum of all first RSRP measurements and the number of all first RSRP measurements, wherein the fifth evaluation index corresponds to the sixth evaluation index;

[0130] Step S323: For each candidate antenna weight in this area, obtain the number of third RSRP prediction values ​​whose values ​​are less than the preset weak coverage threshold among all the third RSRP prediction values ​​corresponding to the current candidate antenna weight in this area, and calculate the seventh evaluation index corresponding to the current candidate antenna weight based on the number of third RSRP prediction values ​​whose values ​​are less than the preset weak coverage threshold and the number of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in this area.

[0131] Step S324: For each candidate antenna weight in this region, obtain the sum of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in this region, and calculate the eighth evaluation index corresponding to the current candidate antenna weight based on the sum and the number of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in this region. The seventh evaluation index corresponds one-to-one with the eighth evaluation index.

[0132] In one embodiment, after acquiring MR data from the terminal, it is first possible to determine which cells belong to weak coverage. Then, based on the antenna weights of these weak coverage cells, evaluation information corresponding to each antenna weight combination is calculated. Specifically, whether the current cell belongs to weak coverage can be determined as follows: under the current antenna weight combination, all MR data are traversed. If the proportion of MR data with a first RSRP measurement value less than a preset weak coverage threshold and a SINR value less than 5dB exceeds 5% of all MR data, then the current cell is considered to be a weak coverage cell. It is worth noting that antenna weight optimization can be performed only on cells belonging to weak coverage; for cells not belonging to weak coverage, antenna weight optimization can be omitted.

[0133] In one embodiment, the fifth evaluation index can be a weak coverage ratio, which can be obtained using the following formula:

[0134]

[0135] Among them, PoorCoverRatio 当前 Q represents the weak coverage ratio under the current antenna weights for this area; 和 Q is the number of first RSRP measurements that are less than a preset weak coverage threshold among all first RSRP measurements. 总 The number of all first RSRP measurements.

[0136] In one embodiment, the sixth evaluation index can be the RSRP average value. Therefore, the sixth evaluation index can be calculated by obtaining the sum of all first RSRP measurements and then calculating the sum of all first RSRP measurements and the number of all first RSRP measurements.

[0137] In one embodiment, the seventh evaluation metric can be the weak coverage ratio. The weight of each candidate antenna in this area can be obtained using the following formula:

[0138]

[0139] Among them, PoorCoverRatio 候选 This represents the weak coverage ratio under the current candidate antenna weights for this area, i.e., the seventh evaluation index corresponding to the current candidate antenna weights for this area; L 和 L is the number of third RSRP prediction values ​​that are less than a preset weak coverage threshold among all the third RSRP prediction values ​​corresponding to the current candidate antenna weights in this area; 总 This represents the number of all third RSRP predictions corresponding to the current candidate antenna weights for this region.

[0140] In one embodiment, the eighth evaluation index can be the RSRP average value. For each candidate antenna weight in the local area, the eighth evaluation index can be calculated by obtaining the sum of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in the local area, and then calculating the sum and the number of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in the local area.

[0141] In one embodiment, once the fifth evaluation index corresponding to the current antenna weight of the current region, the sixth evaluation index corresponding to the current antenna weight of the current region, the seventh evaluation index corresponding to the candidate antenna weight of each region, and the eighth evaluation index corresponding to the candidate antenna weight of each region are obtained, the optimal antenna weight combination can be determined in subsequent steps using these fifth, sixth, seventh, and eighth evaluation indices.

[0142] In another embodiment, when the current scene type is a weak coverage scene, step S400 may include, but is not limited to, the following steps:

[0143] The optimal antenna weight combination is determined by identifying the antenna weight combination that corresponds to the smallest value among the fifth evaluation index and all the seventh evaluation indices.

[0144] In one embodiment, since both the fifth and seventh evaluation indicators are weak coverage ratios, and the smaller the weak coverage ratio, the better the effect of the corresponding antenna weight combination, after calculating the fifth evaluation indicator corresponding to the current antenna weight of the current area and the seventh evaluation indicator corresponding to each candidate antenna weight of the current area, the antenna weight combination corresponding to the smallest value among the fifth evaluation indicator and all the seventh evaluation indicators can be determined as the optimal antenna weight combination.

[0145] It is worth noting that, since the number of configurable candidate antenna weights for each cell is very large (generally greater than 1000), the number of candidate antenna weight combinations for N cells is at least 1000. N Such a large number of combinations makes it impossible to determine the optimal antenna weight combination through traversal. To improve the efficiency of determining the optimal antenna weight combination, this embodiment can use a particle swarm optimization algorithm to optimize the processing time. For example, using the fifth evaluation index and any one of the seventh evaluation indices as the output of the particle swarm optimization algorithm, 100 iterations are performed to find the smallest value among the fifth evaluation index and all the seventh evaluation indices. The antenna weight combination corresponding to this smallest evaluation index can then be considered the optimal antenna weight combination.

[0146] In another embodiment, when the current scene type is a weak coverage scene, step S400 may also include, but is not limited to, the following steps:

[0147] When there are two or more equal values ​​among the fifth evaluation index and all the seventh evaluation indexes, the antenna weight combination corresponding to the largest value among the sixth evaluation index and all the eighth evaluation indexes is determined as the optimal antenna weight combination.

[0148] It is worth noting that the steps in this embodiment and the steps in the above embodiment for determining the antenna weight combination corresponding to the smallest value among the fifth evaluation index and all the seventh evaluation indexes as the optimal antenna weight combination are parallel technical solutions.

[0149] In one embodiment, since the fifth and seventh evaluation indicators are both weak coverage ratio values, and the sixth and eighth evaluation indicators are both RSRP average values, the smaller the weak coverage ratio value or the larger the RSRP average value, the better the effect of the corresponding antenna weight combination. Therefore, when there are two or more equal values ​​among the fifth evaluation indicator and all the seventh evaluation indicators, the antenna weight combination corresponding to the largest value among the sixth evaluation indicator and all the eighth evaluation indicators can be determined as the optimal antenna weight combination.

[0150] It is worth noting that in this embodiment, the particle swarm optimization algorithm can also be used to optimize the processing time of determining the sixth evaluation index and the largest value among all the eighth evaluation indices. When the particle swarm optimization algorithm is used to determine the largest value among the sixth evaluation index and all the eighth evaluation indices, the antenna weight combination corresponding to the evaluation index with the largest value can be considered as the optimal antenna weight combination.

[0151] Additionally, in one embodiment, when the current scenario type is a tidal effect scenario, the evaluation information may include a ninth evaluation index corresponding to the current antenna weight of the current region and a tenth evaluation index corresponding one-to-one with the candidate antenna weights of each region. In this case, refer to Figure 8 Step S300, which calculates the evaluation information corresponding to each antenna weight combination based on all first RSRP information and all second RSRP information, may include, but is not limited to, the following steps:

[0152] Step S325: Obtain the sum of all first RSRP measurements, and calculate the ninth evaluation index based on the sum of all first RSRP measurements and the number of all first RSRP measurements.

[0153] Step S326: For each candidate antenna weight in this region, obtain the sum of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in this region, and calculate the tenth evaluation index corresponding to the current candidate antenna weight based on the sum and the number of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in this region.

[0154] In one embodiment, after acquiring MR data from the terminal, it is first possible to determine which cells belong to the tidal effect scenario. Then, based on the antenna weights of these cells belonging to the tidal effect scenario, evaluation information corresponding to each antenna weight combination is calculated. Specifically, it can be determined whether the current cell belongs to the tidal effect scenario as follows: Under the current antenna weight combination, the number of online users or traffic during peak hours (e.g., from 7:00 AM to 11:00 PM) within a preset time period (e.g., one week or one month) is obtained. Then, the average number of online users or traffic during peak hours and the peak hour fluctuation rate are calculated. If the peak hour fluctuation rate is greater than 5%, the current cell is considered to belong to the tidal effect scenario.

[0155] In one embodiment, the busy-hour volatility can be obtained by the following formula:

[0156] Busy Hour Volatility = abs((Number of Online Users During Busy Hours - Average Number of Online Users During Busy Hours) / Average Number of Online Users During Busy Hours)

[0157] Here, abs() is a function that calculates the absolute value.

[0158] It is worth noting that the preset time period and the duration of the busy period can be appropriately selected according to the actual situation, and this embodiment does not impose specific limitations on them.

[0159] In one embodiment, for cells identified as belonging to a tidal effect scenario, the antenna weight adjustment method can be performed during periods when the busy-hour fluctuation rate is greater than 5%. Furthermore, if a cell belonging to a tidal effect scenario has multiple consecutive periods with busy-hour fluctuation rates greater than 5%, these consecutive periods can be merged into one period for unified antenna weight adjustment; however, if the periods with busy-hour fluctuation rates greater than 5% are not consecutive, antenna weight adjustments can be performed separately for each period.

[0160] In one embodiment, the ninth evaluation index can be the RSRP average value. Therefore, the ninth evaluation index can be calculated by obtaining the sum of all first RSRP measurements and then calculating the sum of all first RSRP measurements and the number of all first RSRP measurements.

[0161] In one embodiment, the tenth evaluation index can be the RSRP average value. For each candidate antenna weight in the local area, the tenth evaluation index can be calculated by obtaining the sum of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in the local area, and then calculating the tenth evaluation index based on the sum and the number of all third RSRP prediction values ​​corresponding to the current candidate antenna weight in the local area.

[0162] In one embodiment, once the ninth evaluation index corresponding to the current antenna weight of the current region and the tenth evaluation index corresponding to the candidate antenna weights of each region are obtained, the optimal antenna weight combination can be determined in subsequent steps using these ninth and tenth evaluation indices.

[0163] In another embodiment, when the current scene type is a tidal effect scene, step S400 may include, but is not limited to, the following steps:

[0164] The optimal antenna weight combination is determined by identifying the antenna weight combination that corresponds to the largest value among the ninth evaluation index and all tenth evaluation indices.

[0165] In one embodiment, since both the ninth and tenth evaluation indices are the average values ​​of RSRP, and the larger the average RSRP, the better the effect of the corresponding antenna weight combination, after calculating the ninth evaluation index corresponding to the current antenna weight of the current region and the tenth evaluation index corresponding to each candidate antenna weight of the current region, the antenna weight combination corresponding to the largest value among the ninth evaluation index and all tenth evaluation indices can be determined as the optimal antenna weight combination.

[0166] It is worth noting that, since the number of configurable candidate antenna weights for each cell is very large (generally greater than 1000), the number of candidate antenna weight combinations for N cells is at least 1000.N Such a large number of combinations makes it impossible to determine the optimal antenna weight combination through traversal. To improve the efficiency of determining the optimal antenna weight combination, this embodiment can use a particle swarm optimization algorithm to optimize the processing time. For example, using the ninth evaluation index and any one of all tenth evaluation indices as the output of the particle swarm optimization algorithm, 100 iterations are performed to find the largest value among the ninth and tenth evaluation indices. The antenna weight combination corresponding to this largest value can then be considered the optimal antenna weight combination.

[0167] In addition, one embodiment of the present invention provides an antenna weighting adjustment device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0168] The processor and memory can be connected via a bus or other means.

[0169] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0170] It should be noted that the antenna weight adjustment device in this embodiment can be applied as follows: Figure 1 The local base station or neighboring base station in the illustrated embodiment, the antenna weight adjustment device in this embodiment can be configured to... Figure 1 The system architecture shown in the embodiments is part of the same inventive concept. Therefore, these embodiments have the same implementation principle and technical effect, which will not be described in detail here.

[0171] The non-transient software program and instructions required to implement the antenna weight adjustment method of the above embodiments are stored in memory. When executed by a processor, the antenna weight adjustment method in the above embodiments is executed, for example, the method described above is executed. Figure 2 Method steps S100 to S500 Figure 3 Method steps S210 to S220, Figure 4 Method steps S311 to S313 in the text Figure 5 Method steps S314 to S416 in the text Figure 6 Method steps S230, Figure 7Method steps S321 to S324 in the text Figure 8 Method steps S325 to S326.

[0172] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by a processor in the above-described device embodiment, causing the processor to perform the antenna weight adjustment method in the above-described embodiment, for example, performing the above-described... Figure 2 Method steps S100 to S500 Figure 3 Method steps S210 to S220, Figure 4 Method steps S311 to S313 in the text Figure 5 Method steps S314 to S416 in the text Figure 6 Method steps S230, Figure 7 Method steps S321 to S324 in the text Figure 8 Method steps S325 to S326.

[0174] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled 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 disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0175] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for adjusting antenna weights, comprising: obtaining a plurality of measurement report (MR) data under a current scene type, wherein each of the MR data comprises first reference signal received power (RSRP) information corresponding to a current antenna weight combination and direction of arrival (DOA) information corresponding to the current antenna weight combination; iterating through all the MR data, and for each of the MR data, calculating second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and the DOA information; wherein the calculating of the second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and the DOA information for each of the MR data comprises: calculating the second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and a power difference value obtained according to the DOA information and a preset three-dimensional beam power table corresponding to the current antenna weight combination, wherein the preset three-dimensional beam power table comprises power values corresponding to antenna weights and DOA values; calculating evaluation information corresponding to each antenna weight combination according to all the first RSRP information and all the second RSRP information; determining an optimal antenna weight combination from the current antenna weight combination and all the candidate antenna weight combinations according to all the evaluation information; updating the current antenna weight combination to the optimal antenna weight combination.

2. The method of claim 1, wherein, In a case where the current scene type is a cluster scene or an overlapping coverage scene, the current antenna weight combination comprises a current antenna weight of a home cell and current antenna weights of each same-frequency neighbor cell; the first RSRP information comprises a first RSRP measurement value corresponding to the current antenna weight of the home cell and second RSRP measurement values corresponding to the current antenna weights of each same-frequency neighbor cell; the DOA information comprises a first DOA measurement value corresponding to the current antenna weight of the home cell and second DOA measurement values corresponding to the current antenna weights of each same-frequency neighbor cell; the candidate antenna weight combination comprises a candidate antenna weight of the home cell and candidate antenna weights of each same-frequency neighbor cell; and the second RSRP information comprises a third RSRP prediction value corresponding to the candidate antenna weight of the home cell and fourth RSRP measurement values corresponding to the candidate antenna weights of each same-frequency neighbor cell. The calculating of the second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and the DOA information comprises: calculating the third RSRP prediction value according to the first RSRP measurement value and the first DOA measurement value; and calculating fourth RSRP prediction values according to the second RSRP measurement values and the second DOA measurement values.

3. The method of claim 2, wherein, In a case where the current scene type is a cluster scene, the evaluation information comprises a first evaluation index corresponding to the current antenna weight combination and second evaluation indices corresponding to each candidate antenna weight combination. The evaluation information corresponding to each antenna weight combination is calculated according to all the first RSRP information and all the second RSRP information, and the evaluation information includes: The first statistical value corresponding to the current antenna weight combination is calculated according to the first RSRP measurement value and all the second RSRP measurement values for each MR data in the MR data set; The first evaluation index is obtained according to all the first statistical values; The second statistical value corresponding to the current candidate antenna weight combination is calculated according to the third RSRP prediction value and all the fourth RSRP prediction values for each MR data in the MR data set, and the second evaluation index corresponding to the current candidate antenna weight combination is obtained according to all the second statistical values after all the MR data are traversed.

4. The method of claim 3, wherein, The optimal antenna weight combination is determined from the current antenna weight combination and all the candidate antenna weight combinations according to all the evaluation information, and the determination includes: The antenna weight combination corresponding to the maximum value of the first evaluation index and all the second evaluation indexes is determined as the optimal antenna weight combination.

5. The method of claim 2, wherein, In the case that the current scene type is an overlapping coverage scene, the evaluation information includes a third evaluation index corresponding to each candidate weight combination and a fourth evaluation index corresponding to each candidate weight combination; The evaluation information corresponding to each antenna weight combination is calculated according to all the first RSRP information and all the second RSRP information, and the evaluation information includes: The candidate weight set is determined from the current antenna weight combination and all the candidate antenna weight combinations according to the first RSRP measurement value, all the second RSRP measurement values, the third RSRP prediction value, all the fourth RSRP prediction values and a first RSRP threshold, the candidate weight set includes a plurality of candidate weight combinations, and the candidate weight combination includes a candidate weight of the current area and candidate weights of each same-frequency neighboring area; The third statistical value corresponding to the current candidate weight combination is calculated according to the RSRP value corresponding to the candidate weight of the current area in the current candidate weight combination and the RSRP value corresponding to each candidate weight of each same-frequency neighboring area in the current candidate weight combination for each MR data in the MR data set, and the third evaluation index corresponding to the current candidate weight combination is calculated according to all the third statistical values corresponding to the current candidate weight combination after all the MR data are traversed. For each of the candidate weight combinations, the number of all RSRP values corresponding to the candidate weight of the current region in the current candidate weight combination that satisfy the overlapping coverage condition is obtained, and the fourth evaluation index corresponding to the current candidate weight combination is calculated according to the number of the RSRP values that satisfy the overlapping coverage condition and the number of all RSRP values corresponding to the candidate weight of the current region in the current candidate weight combination, wherein the third evaluation index and the fourth evaluation index correspond to each other.

6. The method of claim 5, wherein, The determining the optimal antenna weight combination from the current antenna weight combination and all the candidate antenna weight combinations according to all the evaluation information comprises: determining the antenna weight combination corresponding to the fourth evaluation index with the minimum value in all the fourth evaluation indexes as the optimal antenna weight combination; or, when the number of the fourth evaluation indexes with the minimum value in all the fourth evaluation indexes is more than two, determining the antenna weight combination corresponding to the third evaluation index with the maximum value in all the third evaluation indexes corresponding to the fourth evaluation index with the minimum value as the optimal antenna weight combination.

7. The method of claim 1, wherein, In the case that the current scene type is a weak coverage scene or a tidal effect scene, the current antenna weight combination comprises a current antenna weight of a region; the first RSRP information comprises a first RSRP measurement value corresponding to the current antenna weight of the region; the DOA information comprises a first DOA measurement value corresponding to the current antenna weight of the region; the candidate antenna weight combination comprises a candidate antenna weight of the region; and the second RSRP information comprises a third RSRP prediction value corresponding to the candidate antenna weight of the region. The calculating the second RSRP information corresponding to each candidate antenna weight combination according to the first RSRP information and the DOA information comprises: calculating the third RSRP prediction value according to the first RSRP measurement value and the first DOA measurement value.

8. The method of claim 7, wherein, In the case that the current scene type is a weak coverage scene, the evaluation information comprises a fifth evaluation index corresponding to the current antenna weight of the region, a sixth evaluation index corresponding to the current antenna weight of the region, a seventh evaluation index corresponding to each candidate antenna weight of the region, and an eighth evaluation index corresponding to each candidate antenna weight of the region. The calculating the evaluation information corresponding to each antenna weight combination according to all the first RSRP information and all the second RSRP information comprises: obtaining the number of the first RSRP measurement values less than a preset weak coverage threshold in all the first RSRP measurement values, and calculating the fifth evaluation index according to the number of the first RSRP measurement values less than the preset weak coverage threshold and the number of all the first RSRP measurement values; obtaining the sum of all the first RSRP measurement values, and calculating the sixth evaluation index according to the sum of all the first RSRP measurement values and the number of all the first RSRP measurement values, wherein the fifth evaluation index corresponds to the sixth evaluation index; and obtaining the number of the first RSRP measurement values less than a preset weak coverage threshold in all the first RSRP measurement values, and calculating the fifth evaluation index according to the number of the first RSRP measurement values less than the preset weak coverage threshold and the number of all the first RSRP measurement values. For each candidate antenna weight value of the current local area, the number of third RSRP prediction values less than the preset weak coverage threshold is obtained from all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area, and the seventh evaluation index corresponding to the candidate antenna weight value of the current local area is calculated according to the number of third RSRP prediction values less than the preset weak coverage threshold and the number of all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area; For each candidate antenna weight value of the current local area, the sum of all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area is obtained, and the eighth evaluation index corresponding to the candidate antenna weight value of the current local area is calculated according to the sum and the number of all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area, wherein the seventh evaluation index and the eighth evaluation index correspond one by one.

9. The method of claim 8, wherein, The determining the optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations according to all evaluation information comprises: determining the antenna weight combination corresponding to the one with the minimum value in the fifth evaluation index and all seventh evaluation indexes as the optimal antenna weight combination; or, when there are more than two values equal in the fifth evaluation index and all seventh evaluation indexes, determining the antenna weight combination corresponding to the one with the maximum value in the sixth evaluation index and all eighth evaluation indexes as the optimal antenna weight combination.

10. The method of claim 7, wherein, In the case that the current scene type is a tidal effect scene, the evaluation information comprises a ninth evaluation index corresponding to the current antenna weight of the local area and a tenth evaluation index corresponding to each candidate antenna weight of the local area one by one; The calculating the evaluation information corresponding to each antenna weight combination according to all first RSRP information and all second RSRP information comprises: obtaining the sum of all first RSRP measurement values, and calculating the ninth evaluation index according to the sum of all first RSRP measurement values and the number of all first RSRP measurement values; For each candidate antenna weight value of the current local area, the sum of all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area is obtained, and the tenth evaluation index corresponding to the candidate antenna weight value of the current local area is calculated according to the sum and the number of all third RSRP prediction values corresponding to the candidate antenna weight value of the current local area.

11. The method of claim 10, wherein, The determining the optimal antenna weight combination from the current antenna weight combination and all candidate antenna weight combinations according to all evaluation information comprises: determining the antenna weight combination corresponding to the one with the maximum value in the ninth evaluation index and all tenth evaluation indexes as the optimal antenna weight combination.

12. The method of claim 2 or 7, wherein, The calculating the third RSRP prediction value according to the first RSRP measurement value and the first DOA measurement value comprises: The third RSRP prediction value is calculated according to the following formula: RSRP 本区候选 = RSRP 本区当前 + ∂( DOA ) 本区 wherein, RSRP 本区候选 is the third RSRP prediction value, RSRP 本区当前 is the first RSRP measurement value, ∂( DOA ) 本区 is a power difference value obtained according to the first DOA measurement value and a first preset three-dimensional beam power table corresponding to current antenna weight values of the local area, the first preset three-dimensional beam power table including power values corresponding to antenna weight values and DOA values.

13. The method of claim 2, wherein, The fourth RSRP prediction value is calculated according to the second RSRP measurement value and the second DOA measurement value, comprising: The fourth RSRP prediction value is calculated according to the following formula: RSRP 邻区候选 = RSRP 邻区当前 + ∂( DOA ) 邻区 wherein, RSRP 邻区候选 is the fourth RSRP prediction value, RSRP 邻区当前 is the second RSRP measurement value, DOA ) 邻区 is a power difference value obtained according to the second DOA measurement value and a second preset three-dimensional beam power table corresponding to current antenna weights of the same-frequency neighbor cell, the second preset three-dimensional beam power table including power values corresponding to antenna weights and DOA values.

14. An antenna weight adjustment apparatus comprising: Memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the antenna weight adjustment method according to any one of claims 1 to 13 when executing the computer program.

15. A computer readable storage medium storing computer executable instructions for performing the antenna weight adjustment method according to any one of claims 1 to 13.

Images