Antenna weight value determination method and apparatus, and storage medium and electronic apparatus

Abstract

Provided in the embodiments of the present disclosure are an antenna weight value determination method and apparatus, and a storage medium and an electronic apparatus. The method comprises: according to user terminal data and channel state information, identifying the distribution of user terminals in a cell in different channel states; and on the basis of the distribution of the user terminals, determining an optimal antenna weight value in the different channel states.

Description

Antenna weight determination method, device, storage medium and electronic device Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of network coverage optimization, and in particular to a method, device, storage medium, and electronic device for determining antenna weights. Background Art

[0002] To reduce energy consumption and waste, intelligent energy-saving solutions are often adopted in some antenna usage scenarios. For example, some antenna channels can be shut down to achieve channel shutdown energy saving. However, if the channel is shut down without modifying the antenna weight, the antenna coverage range will change. The change in coverage range will affect the normal communication between various network devices. Therefore, to ensure the stability of the communication environment, the antenna needs to maintain optimal network coverage when the channel shutdown state changes.

[0003] It is known that preset antenna weights cannot cope with various coverage scenarios. Therefore, to cope with various coverage scenarios and achieve coverage optimization, the following method is commonly used in related technologies: collecting MR (Measurement Report) data from user terminals and estimating the coverage under various antenna weights. Then, an AI (Artificial Intelligence) optimization algorithm is used to select the antenna weights that can achieve the best coverage. However, this optimization process is not applicable to scenarios where the channel shutdown energy-saving function is enabled. This is because the key technology used by the algorithm is to estimate the coverage effect under other weights based on the antenna gain under each weight. However, when the channel is shut down, the original antenna gain has changed. The coverage pattern formed by the same antenna weight in the channel shutdown and non-shutdown scenarios is also different, and the estimated coverage effect under other weights is biased. Therefore, when antenna weight optimization and channel shutdown energy saving are enabled at the same time, the antenna weights optimized by the AI ​​optimization algorithm may not be the optimal result, thereby affecting the network coverage effect.

[0004] Summary of the Invention

[0005] The embodiments of the present disclosure provide a method, device, storage medium, and electronic device for determining antenna weights to at least solve the problem that existing antenna weight adjustment schemes in related technologies cannot be applied to channel shutoff and energy-saving activation scenarios.

[0006] According to one embodiment of the present disclosure, a method for determining antenna weights is provided, comprising: identifying the distribution of user terminals in a cell under different channel states based on user terminal data and channel state information; and determining optimal antenna weights under the different channel states based on the user terminal distribution.

[0007] According to another embodiment of the present disclosure, an antenna weight determination device is provided, including: an identification module, configured to identify the distribution of user terminals in a cell under different channel states based on user terminal data and channel state information; and a determination module, configured to determine the optimal antenna weights under the different channel states based on the user terminal distribution.

[0008] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0009] According to another embodiment of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG1 is a hardware structure block diagram of a network device for executing a method for determining antenna weights according to an embodiment of the present disclosure;

[0011] FIG2 is a schematic diagram of a network architecture for executing a method for determining antenna weights according to an embodiment of the present disclosure;

[0012] FIG3 is a flow chart of a method for determining antenna weights according to an embodiment of the present disclosure;

[0013] FIG4 is a structural block diagram of an apparatus for determining antenna weights according to an embodiment of the present disclosure;

[0014] FIG5 is a flow chart of a method for self-adjusting antenna weights according to an embodiment of the present disclosure, which method can be applied to a channel shutoff scenario;

[0015] 6 is a flow chart of an optimization strategy when the distribution of channel-off and non-channel-off user terminals is consistent according to an embodiment of the present disclosure;

[0016] FIG7 is a flowchart of an optimization strategy when the distribution of channel-off and non-channel-off user terminals is inconsistent according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal, a network device or a similar computing device. Taking operation on a network device as an example, FIG1 is a hardware structure block diagram of a network device that runs the antenna weight determination method in an embodiment of the present disclosure. As shown in FIG1 , the network device may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor or a programmable logic device) and a memory 104 for storing data, wherein the above-mentioned network device may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that the structure shown in FIG1 is only for illustration and does not limit the structure of the above-mentioned network device. For example, the network device may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

[0020] Memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the antenna weight determination method in the embodiments of the present disclosure. Processor 102 executes the computer programs stored in memory 104 to execute various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located relative to processor 102, and such remote memory may be connected to a network device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0021] The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by a communications provider of the network device. In one embodiment, the transmission device 106 may include a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0022] The embodiment of the present application can run on the network architecture shown in Figure 2. As shown in Figure 2, the network architecture includes: user terminals, network elements, and network element management equipment.

[0023] The network element is configured to collect user terminal data and cell channel shutdown status information; wherein the user terminal data is the signal strength and direction relative to the cell measured by the terminal, and is configured to be data representing the distribution of user terminals; the channel shutdown status information is the shutdown status of the antenna channel when the terminal reports the user terminal data.

[0024] A network element management device configured to collect and store user terminal data and cell channel status information, and to initiate user terminal distribution identification after data collection is completed;

[0025] Specifically, user terminal distribution identification includes: identifying the distribution of user terminals based on collected user terminal data and cell channel status information, and determining whether the distribution of user terminals under channel shutdown and non-channel shutdown conditions is consistent.

[0026] For cells where the distribution of user terminals in channel-off and non-channel-off scenarios is consistent, the network element management device automatically converts data, converting the user terminal data corresponding to the channel-off scenario to the corresponding non-channel-off scenario, so as to unify the user terminal data in different scenarios and solve the problem of antenna gain changes after channel shutdown. Direct use of the original user terminal data for calculations will lead to inaccurate input data and deviations in the calculation results. The converted user data is combined with the user data in the non-channel-off state to optimize the antenna weights to optimize a set of optimal antenna weights. This antenna weight is suitable for the non-channel-off scenario and outputs the optimal antenna weights in the non-channel-off scenario. Based on the mapping relationship between the channel-off weights and the non-channel-off weights under the same coverage pattern of the network element management device, a suitable set of weights is matched for channel shutdown.

[0027] The antenna weights are automatically switched and optimized according to the antenna status, so that the coverage range does not change before and after the channel is shut down and remains optimal, matching the user terminal distribution to achieve the best coverage effect, and solving the problem that the antenna coverage range will change if the weights are not changed after the channel is shut down.

[0028] For cells with inconsistent user terminal distributions in channel-shutdown and non-channel-shutdown scenarios, the same set of antenna coverage patterns cannot cope with different user terminal distributions. Therefore, channel-shutdown and non-channel-shutdown scenarios cannot be optimized together. The network element management device needs to distinguish user terminal data under different channel-shutdown scenarios and use these data to optimize a set of antenna weights for different channel-shutdown scenarios. The two sets of weights have different coverage ranges. The weights for the channel-shutdown scenario match its user terminal distribution, and the weights for the non-channel-shutdown scenario match its user terminal distribution, to solve the scenario where the user terminal distribution changes after the shutdown.

[0029] The network management device is also used to generate an optimization strategy, wherein generating the optimization strategy includes: automatically adapting the antenna weights that are consistent with the user terminal distribution of the cell according to the channel shutdown state of the cell, so that the antenna weights match the user terminal distribution.

[0030] Among them, the antenna weight is composed of the direction angle, tilt angle, horizontal beam width, and vertical beam width. Different antenna weights will form different coverage patterns and present different wireless signal coverage effects.

[0031] In this embodiment, a method for determining antenna weights running on the above-mentioned network device or network architecture is provided. FIG3 is a flow chart of the method for determining antenna weights according to an embodiment of the present disclosure. As shown in FIG3 , the flow chart includes the following steps:

[0032] Step S302: Identify the distribution of user terminals in the cell under different channel states based on the user terminal data and channel state information, wherein the channel state information includes: channel-off state and non-channel-off state.

[0033] The channel shutoff state includes: a plurality of channel shutoff states after shutting down channels according to different channel shutoff ratios, and the channel shutoff ratio is a ratio of the number of shutoff channels to the total number of antenna channels.

[0034] In an exemplary embodiment, the total number of channels of the antenna is a multiple of the number of channels that the antenna is turned off.

[0035] For example, in a 5G network environment, the current antenna types mainly include 64-channel, 32-channel, 16-channel, 8-channel, and 4-channel. For each of the above types of antennas, the channel shutdown states generally include: shutting down a quarter of the channel, shutting down a half of the channel, and shutting down three-quarters of the channel.

[0036] For another example, for 64 channels, in addition to the above channel shut-off states, the following states are also included: shutting down one-eighth of the channels, shutting down one-sixteenth of the channels, and shutting down one-thirty-second of the channels.

[0037] The shutdown states of other types of antennas also include other channel shutdown states in addition to the above-mentioned channel shutdown states. Please refer to the example described for the above-mentioned 64-channel type antenna, and this embodiment will not be repeated here.

[0038] In this embodiment, the user terminal data includes: the reference signal receiving power (RSRP), horizontal direction of arrival (HDOA), and vertical direction of arrival (VDOA) of the current cell in which the user terminal is located, as well as the RSRP, HDOA, and VDOA of the neighboring cells of the current cell.

[0039] Before step S302 of this embodiment, the method further includes: dividing cells whose area overlap between cells meets a threshold condition into an optimization cluster, and determining the antenna weights based on the optimization cluster.

[0040] In one embodiment, identifying the distribution of user terminals in a cell under different channel states includes: for any cell in any of the optimized clusters, grouping the user terminal data in the cell according to different channel states, wherein each of the cells includes at least one channel state; rasterizing the user terminal data in each group based on the horizontal direction of arrival (HDOA) and the vertical direction of arrival (VDOA) of the cell to obtain rasterized data corresponding to the different channel states; respectively obtaining similarities of the rasterized data between different channel-off states and the non-channel-off states; and identifying the distribution of user terminals in the cell under different channel states based on the similarities.

[0041] Among them, the distribution of user terminals in the cell under different channel-off states includes one of the following: the distribution of user terminals in all the channel-off states is consistent with that in the non-channel-off state; the distribution of user terminals in all the channel-off states is inconsistent with that in the non-channel-off state; the distribution of user terminals in some of the channel-off states is consistent with that in the non-channel-off state; the distribution of user terminals in some of the channel-off states is inconsistent with that in the non-channel-off state.

[0042] Step S304: determining optimal antenna weights under the different channel states based on the user terminal distribution.

[0043] In one embodiment, when the distribution of user terminals in all the channel-off states is consistent with that in the non-channel-off state, determining the optimal antenna weights in the different channel states includes: for any of the channel-off states in the cell, converting the reference signal received power RSRP data collected in the channel-off state into first RSRP data in the non-channel-off state; estimating second RSRP data under multiple different antenna weights based on the first RSRP data and a gain table; and comparing the first RSRP data with the second RSRP data, and selecting the antenna weight corresponding to the maximum RSRP data as the optimal antenna weight in the channel-off state.

[0044] In one embodiment, when the distribution of user terminals in all the channel-off states is inconsistent with that in the non-channel-off state, determining the optimal antenna weights in the different channel states includes: for any of the channel-off states in the cell, based on the third RSRP data and the gain table in the channel-off state, estimating fourth RSRP data under multiple different antenna weights; comparing the third RSRP data with the fourth RSRP data, and selecting the antenna weight corresponding to the maximum RSRP data as the optimal antenna weight in the channel-off state.

[0045] In one embodiment, when the distribution of user terminals in some of the channel-off states is consistent with that in the non-channel-off states, determining the optimal antenna weights in the different channel states includes: in the cell, for the channel-off state in which the user terminal distribution is consistent with the non-channel-off state, converting the RSRP data collected in the channel-off state into fifth RSRP data of the non-channel-off state; and based on the fifth RSRP data and the gain table, estimating sixth RSRP data under multiple different antenna weights; comparing the fifth RSRP data with the sixth RSRP data, and selecting the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel-off state.

[0046] In one embodiment, when the user terminal distribution in some of the channel-off states is inconsistent with that in the non-channel-off states, the optimal antenna weights in the different channel states are determined, including: for the channel-off state in which the user terminal distribution is inconsistent with the non-channel-off state, based on the seventh RSRP data and the gain table in the channel-off state, estimating the eighth RSRP data under multiple different antenna weights; and comparing the seventh RSRP data with the eighth RSRP data, selecting the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel-off state.

[0047] In one embodiment, the method further comprises determining the optimal antenna weights under the different channel states, and determining the antenna weights corresponding to the RSRP data collected in the non-channel-off state as the optimal antenna weights in the non-channel-off state.

[0048] After step S304 of this embodiment, the method further includes: according to the current channel state of the cell, switching the antenna weight of the cell to an optimal antenna value corresponding to the current channel state.

[0049] Through the above steps, the distribution of user terminals in a cell under different channel states is identified based on user terminal data and channel state information, and the optimal antenna weights for each channel state are determined based on this user terminal distribution. As can be seen, the inclusion of channel state information in the process of determining the optimal antenna weights for each channel state can address the issue of existing antenna weight adjustment schemes in the related art being inapplicable to scenarios where channel power is turned off and energy saving is enabled, thereby achieving accurate determination of the optimal antenna weights.

[0050] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0051] This embodiment also provides an antenna weight determination device for implementing the aforementioned embodiments and preferred implementations. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0052] FIG4 is a structural block diagram of an antenna weight determination device according to an embodiment of the present disclosure. As shown in FIG4 , the device is used to execute the antenna weight determination method in any of the above embodiments, and includes: an identification module 10 and a determination module 20 .

[0053] An identification module 10 is configured to identify the distribution of user terminals in cells under different channel states based on user terminal data and channel state information;

[0054] The channel status information includes: a channel shutdown state and a non-channel shutdown state; the channel shutdown state includes: multiple channel shutdown states after channel shutdown according to different channel shutdown ratios, and the channel shutdown ratio is the ratio of the number of shutdown channels to the total number of antenna channels.

[0055] The determination module 20 is configured to determine the optimal antenna weights under the different channel states based on the user terminal distribution.

[0056] In one embodiment, the identification module 10 includes:

[0057] a grouping unit configured to group the user terminal data in any cell in any optimized cluster according to different channel states, wherein the optimized cluster is composed of multiple cells whose area overlap between cells meets a threshold condition, and each cell in the optimized cluster includes at least one channel state;

[0058] a processing unit configured to perform rasterization processing on the user terminal data in each group based on the horizontal direction of arrival (HDOA) and the vertical direction of arrival (VDOA) of the cell to obtain rasterized data corresponding to the different channel states;

[0059] An acquiring unit, configured to respectively acquire similarities of the rasterized data between different channel-off states and the non-channel-off states;

[0060] The identification unit is configured to identify the distribution of user terminals in different channel states in the cell based on the similarity.

[0061] In one embodiment, the distribution of user terminals in the cell under different channel-off states includes one of the following: all the channel-off states are consistent with the distribution of user terminals in the non-channel-off state; all the channel-off states are inconsistent with the distribution of user terminals in the non-channel-off state; some of the channel-off states are consistent with the distribution of user terminals in the non-channel-off state; some of the channel-off states are inconsistent with the distribution of user terminals in the non-channel-off state.

[0062] In an exemplary embodiment, when the distribution of all the user terminals in the channel-off state is consistent with the distribution of the user terminals in the non-channel-off state, the determining module 20 includes:

[0063] A first conversion unit is configured to convert, for any of the channel off states in the cell, the reference signal received power RSRP data collected in the channel off state into first RSRP data in a non-channel off state;

[0064] A first estimating unit is configured to estimate and obtain second RSRP data under a plurality of different antenna weights based on the first RSRP data and a gain table;

[0065] The first comparison unit is configured to compare the first RSRP data and the second RSRP data, and select the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel off state.

[0066] In an exemplary embodiment, when the distribution of user terminals in all the channel-off states is inconsistent with the distribution of user terminals in the non-channel-off state, the determining module 20 includes:

[0067] A second estimating unit is configured to estimate, for any of the channel off states in the cell, fourth RSRP data under a plurality of different antenna weights based on the third RSRP data in the channel off state and the gain table;

[0068] The second comparison unit is configured to compare the third RSRP data and the fourth RSRP data, and select the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel off state.

[0069] In an exemplary embodiment, when the distribution of some of the user terminals in the channel-off state is consistent with that in the non-channel-off state, the determining module 20 includes:

[0070] The second conversion unit is configured to, in the cell, for any of the channel-off states in which the user terminal distribution is consistent with the non-channel-off state, convert the RSRP data collected in the channel-off state into fifth RSRP data of the non-channel-off state;

[0071] A third estimating unit estimates and obtains sixth RSRP data under a plurality of different antenna weights based on the fifth RSRP data and the gain table;

[0072] The third comparing unit compares the fifth RSRP data with the sixth RSRP data, and selects the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel off state.

[0073] In an exemplary embodiment, when the distribution of some of the user terminals in the channel-off state is inconsistent with the distribution of the user terminals in the non-channel-off state, the determining module 20 includes:

[0074] a fourth estimating unit, configured to estimate, for any of the channel-off states whose user terminal distribution is inconsistent with the non-channel-off state, eighth RSRP data under a plurality of different antenna weights based on the seventh RSRP data in the channel-off state and the gain table;

[0075] The fourth comparing unit is configured to compare the seventh RSRP data with the eighth RSRP data, and select the antenna weight corresponding to the largest RSRP data as the optimal antenna weight in the channel off state.

[0076] In one embodiment, the determination module 20 further includes: a determination unit configured to determine the antenna weight corresponding to the RSRP data collected in the non-channel shutoff state as the optimal antenna weight in the non-channel shutoff state.

[0077] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0078] To facilitate understanding of the technical solutions provided by the present disclosure, embodiments of specific scenarios will be described in detail below.

[0079] In a 5G network environment, there are multiple antenna channel shutdown energy-saving strategies, such as shutting down a quarter of the channel, shutting down a half of the channel, and shutting down three-quarters of the channel. Each channel shutdown energy-saving strategy corresponds to a set of antenna weights and an antenna weight gain table.

[0080] The gain table is built into the processing system of the network management device, and includes: a gain table of each antenna in the non-shutdown state and a gain table in the shutdown state, for example: a gain table when shutting down 1 / 4, 1 / 2, and 3 / 4.

[0081] Each antenna type has a set of antenna gain tables (i.e., a gain table in the non-shutdown state and a gain table in the shutdown state). The table is fixed when the number of antennas is fixed.

[0082] The format of the gain table is shown in Table 1. In this table, the HDOA-Angle range is: -90 to 90, and the VDOA-Angle range is: -45 to 45. Table 1 is only an example and only shows some antenna weights.

[0083] Table 1

[0084] Each type of antenna has multiple sets of table formats as shown in Table 1, for example:

[0085] For a 64-channel antenna, it includes: 64T non-shutoff weight gain table, 64T shutoff 1 / 4 weight gain table, 64T shutoff 1 / 2 weight gain table, and 64T shutoff 3 / 4 weight gain table.

[0086] For a 32-channel antenna, it includes: 32T non-shutoff weight gain table, 32T shutoff 1 / 4 weight gain table, 32T shutoff 1 / 2 weight gain table, and 32T shutoff 3 / 4 weight gain table.

[0087] Similar to the above-mentioned 64-channel antenna or 32-channel antenna, other types of antennas also include a set of antenna gain tables corresponding to different shutdown strategies.

[0088] Table 2 shows the correspondence between the non-channel-off antenna weights and the channel-off weights for each antenna. This table is built into the network management system. For the same coverage pattern, different numbers of channels correspond to fixed antenna weights and a fixed number of antennas. This table is fixed.

[0089] Table 2

[0090] In this embodiment, there are as many types of the above-mentioned corresponding relationships as there are groups of weights.

[0091] Each W in Table 2 corresponds to multiple sub-beams, and each sub-beam consists of a direction angle, a downtilt angle, a horizontal beamwidth, and a vertical beamwidth. For example: W is 8 beams, SubBeam is sub-beam: SubBeam1 azimuth='-44', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam2 azimuth='-31', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam3 azimuth='-18', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam4 azimuth='-6', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam5 azimuth='6', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam6 azimuth='18', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam7 azimuth='31', tilt='-1', beamWidthH='10', beamWidthV='6'; SubBeam8 azimuth='44', tilt='-1', beamWidthH='10', beamWidthV='6';

[0092] The embodiments of the present disclosure provide a method for automatically adjusting antenna weights. This method automatically identifies the characteristics of the user terminal distribution in the channel-off energy-saving state and the user terminal distribution in the non-channel-off state, thereby automatically adapting different optimization strategies to the user terminal distributions in different states, so that the wireless coverage is consistent with the user terminal distribution to achieve optimal coverage, and can solve the problem that the channel-off energy-saving state affects the optimization effect of the antenna weights.

[0093] This method is applicable to wireless network optimization scenarios and can optimize wireless coverage effects in energy-saving states such as channel shutdown or changes that affect antenna gain.

[0094] FIG5 is a flow chart of a method for self-adjusting antenna weights according to an embodiment of the present disclosure. The method can be applied to a channel shutoff scenario. As shown in FIG5 , the method includes the following steps:

[0095] Step S501: Select an optimization target range and start data collection of network elements.

[0096] Step S502: The network element collects user terminal data and channel status information.

[0097] The user terminal data is the signal strength and direction relative to the cell measured by the user terminal, which is used to characterize the distribution of the user terminal. It includes: RSRP, HDOA, and VDOA of the cell where the user terminal is located, as measured by the user terminal, as well as RSRP, HDOA, and VDOA information of the neighboring cells of the cell.

[0098] The channel status information is the antenna channel shutdown status when the user terminal reports the user terminal data.

[0099] Step S503: The network element management device collects and stores user terminal data and cell channel shutdown status information.

[0100] Step S504: using an optimization clustering algorithm, cells with similar relationships are grouped into an optimization cluster, and adjustment and optimization are performed in units of the optimization cluster.

[0101] Because antenna weight adjustment may affect other cells, resulting in overlapping coverage and over-coverage, it is necessary to group cells first and adjust the cells in the group together to consider the impact between cells.

[0102] Specifically, cells whose area overlap between cells meets a threshold condition are divided into an optimization cluster.

[0103] In step S505, the network element management device identifies the user terminal distribution based on the user terminal device data and the information on the cell channel shutdown status, and determines whether the channel shutdown strategies are the same as the non-channel shutdown user terminal distribution. If they are the same, the optimization strategy of step S506 is adopted; if they are different, the optimization strategy of step S507 is adopted.

[0104] Coverage optimization is primarily based on user terminal distribution. The goal is to align antenna weight coverage with the user terminal distribution to achieve optimal coverage. This embodiment uses self-identification of user terminal distribution, identifying the consistency of distribution in both off-channel and on-channel states. This allows different optimization strategies to be selected based on this characteristic.

[0105] Specifically, taking the optimized cluster as the unit, self-identification includes the following steps:

[0106] In step S5051, the user terminal data of each cell is grouped according to different channel shutdown states, for example, grouped according to channel shutdown 1 / 4, through shutdown 1 / 2, channel shutdown 3 / 4 and not shut down; wherein, the channel shutdown strategy of each cell may be different, there may be only two or only one.

[0107] Step S5052: Divide the data of each group into a two-dimensional grid based on HDOA and VDOA, and calculate the proportion of MR user data in each grid. MR user data includes RSRP, HDOA, and VDOA.

[0108] Specifically, HDOA uses the step size of HorizontalBeamGridSpace, and VDOA uses the step size of VerticalBeamGridSpace for rasterization, and the MR proportion in each grid is counted.

[0109] Then expand the two-dimensional grid data into a one-dimensional vector. The one-dimensional vector of the MR data grid for channel shutdown energy saving is as follows:

[0110] X1n=[x11,x12,x13,……x1n] turns off 1 / 4 channels

[0111] X2n=[x21,x22,x23,……x2n] turns off 1 / 2 channels

[0112] X3n=[x31,x32,x33,……x3n] turns off channel 3 / 4

[0113] X0n=[x01,x02,x03,……x0n] non-channel shutdown

[0114] Step S5053, calculating the similarity between the rasterized data when each channel is turned off and the data when the channel is not turned off;

[0115] For example, by calculating the Euclidean distance between two vectors, the closer the distance, the greater the similarity. Setting a distance threshold controls the calculation of similarity to cope with different usage scenarios. The formula for calculating the Euclidean distance is shown in formula (1).

[0116] Step S506, when the distribution of user terminals in the channel-off and non-channel-off scenarios is consistent, the data collected in the channel-off scenario is processed, and the processed user terminal data of the channel-off scenario is optimized together with the user terminal data collected in the non-channel-off scenario to obtain a set of weights. The weights are used to take effect in the non-channel-off state, and the corresponding weights are queried based on the mapping relationship between the channel-off weights and the non-channel-off weights in the channel-off state.

[0117] The algorithm idea of ​​the optimization strategy in this step is:

[0118] Since the antenna gain will change after the channel is shut down, the user terminal data in the non-channel shut-off state reported by the original user terminal can no longer be used as the benchmark data for calculation. The user terminal data in the channel shut-off state needs to be converted into the corresponding user terminal data in the non-channel shut-off state, and the antenna weight optimization is performed together with the data in the non-channel shut-off state reported by the terminal user to optimize a weight suitable for the coverage range of the non-channel shut-off scenario. Then, according to the weight relationship between channel shut-off and non-channel shut-off under the same coverage range (i.e., Table 1 and Table 2), the weight under channel shut-off is matched and automatically takes effect.

[0119] Specifically, FIG6 is a flowchart of an optimization strategy when the distribution of channel-off and non-channel-off user terminals is consistent according to an embodiment of the present disclosure. As shown in FIG6 , the process includes the following steps:

[0120] Step S601, filtering the user terminal data in the channel off state to group the user terminal data of each cell according to the different channel off states of each cell;

[0121] Step S602: Process the RSRP data in the channel-off state and convert it into the corresponding data in the non-channel-off state to obtain RSRP CDF50, where the conversion formula is as follows: RSRP = RSRP s +delta-RSRP(VDOA, HDOA) formula (2)

[0122] Among them, RSRP s : RSRP value collected when the channel is shut down;

[0123] Delta-RSRP (VDOA, HDOA): The difference between the antenna gain of the cell's non-channel-off SSB weight and the antenna gain of the channel-off SSB (Single Side Band) weight corresponding to the sample.

[0124] Step S603: Calculate the optimal antenna weights using an intelligent optimization algorithm to determine the maximum RSRP CDF50. The data sources are the RSRP data in the channel-off state and the RSRP data in the non-channel-off state converted in step S601.

[0125] Specifically, this embodiment uses an ant colony algorithm to calculate the optimal antenna weights. The algorithm's inputs are the RSRP data in the channel-off state after data processing in step S602 and the RSRP data reported in the non-channel-off state. The optimization objective is to achieve the optimal RSRP cdf50, i.e., optimal coverage. The following optimization is performed on a cluster-by-cluster basis.

[0126] Specifically, the optimization algorithm includes the following steps:

[0127] Step 6031 : Calculate RSRP CDF50 under the currently configured antenna weights, the data source being the channel-off state data converted in step S602 and the RSRP data in the non-channel-off state.

[0128] Step 6032: Enter the ant colony iteration algorithm, set the number of ants N and the number of iterations M, and estimate the RSRP CDF50 under each set of weights. The estimation formula is as follows: RSRP wi =RSRP+AntGain i -AntGain formula (3)

[0129] RSRP: RSRP output by S602 and RSRP reported in the non-channel off state;

[0130] RSRP wi : estimated RSRP when the weight is wi;

[0131] AntGain i : Look up the gain table to obtain the corresponding gain under the weight value of wi (HDOA, VDOA);

[0132] AntGain: The gain corresponding to the w weight (HDOA, VDOA) obtained by looking up the gain table;

[0133] Step 6033 calculates RSRP cdf50 and outputs the weighted combination with the largest RSRP CDF50 in this iteration.

[0134] Specifically, the maximum RSRP CDF 50 is selected from the RSRP CDF 50 under the currently configured antenna weights and the RSRP CDF 50 under each group of weights through the ant colony algorithm.

[0135] Step 6034 enters the next iteration of the ant colony algorithm with the optimal weight combination of the previous iteration until the number of iterations meets the end condition and outputs the optimal weight combination.

[0136] Step S604 , based on the optimal weight combination outputted in S603 and by looking up Table 1 and Table 2, obtain antenna weights for channel shutoff 1 / 4, channel shutoff 1 / 2, and channel shutoff 3 / 4.

[0137] In step S605, the antenna weights take effect on the network element device, and the network element switches the antenna weights in a timely manner according to the current channel shutdown state to obtain the best coverage effect.

[0138] Step S507: When the distribution of user terminals in channel-off and non-channel-off conditions is inconsistent, the data collected in non-channel-off conditions are used for optimization to optimize the antenna weights in non-channel-off conditions; and then the data in channel-off conditions are used to optimize different antenna weights for each channel-off strategy.

[0139] The algorithm idea of ​​the optimization strategy in this step is:

[0140] When the distribution of user terminals in the channel-off state and the non-channel-off state is different, one coverage range can no longer adapt to multiple user terminal distributions, so it is necessary to optimize a coverage range for each user terminal distribution to improve the coverage effect.

[0141] FIG7 is a flow chart of an optimization strategy when the distribution of channel-off and non-channel-off user terminals is inconsistent according to an embodiment of the present disclosure. As shown in FIG7 , the flow includes the following steps:

[0142] Step S701, filtering the user terminal data in the channel off state;

[0143] Step S702: Calculate the optimal weights of cells in the optimization cluster under different channel shutdown states.

[0144] For example, there are three cells C1, C2, and C3 in the optimization cluster. C1 has two strategies (shutdown 1 / 4 and not shut down), C2 has three strategies (shutdown 1 / 4, shutdown 1 / 2, and not shut down), and C3 has one strategy (not shut down). Then calculate the cell weights with the smallest combination of various strategies separately.

[0145] The weights of C1 shutting off 1 / 4, C2 shutting off 1 / 4, and C3 not shutting off are calculated in the same way as in step S603;

[0146] The weights for other combinations are calculated in the same way as above. There are 6 combinations in total, and 6 sets of weights are calculated.

[0147] Step S703, calculating the weight combinations of other optimized clusters using the above method;

[0148] Step S704: according to the channel shutdown state of the network element, the antenna weight is switched in real time to take effect, so as to meet the coverage of different user terminal distributions using different antenna weights to achieve the best coverage effect.

[0149] Scenario Example 1:

[0150] In this embodiment, the distribution of user terminals in the cell in the optimized cluster in the channel-off state is consistent with that in the non-channel-off state.

[0151] There are three cells C1, C2, and C3 in the optimized cluster;

[0152] C1 has two strategies: channel shutdown 1 / 4 and non-channel shutdown;

[0153] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0154] C3 has 1 strategy: non-channel shutdown.

[0155] Step S1: Calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0156] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell.

[0157] First, divide the grid into two dimensions: HDOA uses HorizontalBeamGridSpace = 60, and VDOA uses VerticalBeamGridSpace = 20. By calculating the percentage of each grid, we can obtain the distribution of user terminals in different channel shutdown states in each cell.

[0158] The distribution of user terminals with non-channel shutdown in C1 is shown in Table 3:

[0159] Table 3

[0160] The distribution of user terminals with 1 / 4 of the C1 channel shut down is shown in Table 4:

[0161] Table 4

[0162] Step S2, calculate the Euclidean distance according to the above formula (1) to determine the similarity.

[0163] Assuming that the similarity distance threshold is 0.3 and 0.1<0.3, the user terminal distribution when none of the C1 channels are disconnected is the same as the user terminal distribution when 1 / 4 of the C1 channels are disconnected.

[0164] Calculate in the same way:

[0165] The Euclidean distance between the user terminal distribution when 1 / 4 of the C2 channels are shut down and the user terminal distribution when none of the C2 channels are shut down is 0.15.

[0166] The Euclidean distance between the user terminal distribution when half of the C2 channel is off and the user terminal distribution when the C2 channel is not off is 0.2, both below the threshold of 0.3. This means that the user terminal distribution when the C2 channel is not off is the same as the user terminal distribution when the C2 channel is off.

[0167] In the non-channel shutdown state, the C3 cell has only one state and does not need to calculate the distance.

[0168] Step S3, convert RSRP using formula (2) in step S602

[0169] Convert the RSRP when the C1 channel is shut down 1 / 4 to the RSRP in the non-shutdown state;

[0170] Convert the RSRP when the C2 channel is shut down 1 / 4 to the RSRP in the non-shutdown state;

[0171] Convert the RSRP when the C2 channel is shut down 1 / 2 to the RSRP in the non-shutdown state;

[0172] Step S4, through steps S603-S604, calculate the optimal weights of C1, C2, and C3 in the non-shutdown state: C1 is optimal w1, C2 is optimal w2, and C3 is optimal w3;

[0173] Step S5, query the weight w11 corresponding to w1 when the channel is closed 1 / 4; the weight w21 corresponding to w2 when the channel is closed 1 / 4; and the weight w22 corresponding to w2 when the channel is closed 1 / 2.

[0174] That is, the weight of cell C1 is w1 when it is in the non-channel-off state, and w11 when it is 1 / 4 off;

[0175] The weight of cell C2 in the non-channel shutoff state is w2, the weight when 1 / 4 is shut off is w21, and the weight when 1 / 2 is shut off is w22;

[0176] The optimal weight of C3 is w3.

[0177] In an exemplary embodiment, the number of divided grids is greater than 8.

[0178] Scenario Example 2:

[0179] In this embodiment, the distribution of user terminals in the cell channel-off state within the optimization cluster is inconsistent with the distribution in the cell channel-off state.

[0180] There are three cells C1, C2, and C3 in the optimized cluster;

[0181] C1 has two strategies: channel shutdown 1 / 4 and non-channel shutdown;

[0182] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0183] C3 has 1 strategy: non-channel shutdown.

[0184] Step S1: Calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0185] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell.

[0186] First, divide the grid into two dimensions: HDOA uses HorizontalBeamGridSpace = 60, and VDOA uses VerticalBeamGridSpace = 20. By calculating the percentage of each grid, we can obtain the distribution of user terminals in different channel shutdown states in each cell.

[0187] The distribution of user terminals with non-channel shutdown in C1 is shown in Table 5:

[0188] Table 5

[0189] The distribution of user terminals with 1 / 4 of the C1 channel shut down is shown in Table 6:

[0190] Table 6

[0191] Step S2, calculate the Euclidean distance according to the above formula (1) to determine the similarity

[0192] Assume that the similarity distance threshold is 0.3. If 0.41>0.3, it means that the user terminal distribution when the C1 channel is not shut down is different from the user terminal distribution when the C1 channel is shut down 1 / 4.

[0193] Calculate in the same way:

[0194] The Euclidean distance between the user terminal distribution when 1 / 4 of the C2 channels are shut down and the user terminal distribution when no C2 channels are shut down is 0.35.

[0195] The Euclidean distance between the user terminal distribution when 1 / 2 of the C2 channels are turned off and the user terminal distribution when the C2 channels are not turned off is 0.3, both of which are greater than the threshold of 0.3, indicating that the user terminal distribution when the C2 channels are not turned off is different from the user terminal distribution when the C2 channels are turned off.

[0196] In the non-channel shutdown state, the C3 cell has only one state and does not need to calculate the distance.

[0197] Step S3: Through step S507, the optimal weights of C1, C2, and C3 when each channel is turned off are calculated, as shown in Table 7:

[0198] Table 7

[0199] Step S4: The optimal antenna weights of C1, C2, and C3 calculated in step S3 in each channel off state and non-channel off state are validated in the network element, so that the corresponding weights are automatically validated when the cell switches to different state combinations.

[0200] Scenario Example 3:

[0201] In this embodiment, the distribution of user terminals in the cell channel-off state and the distribution of user terminals in the cell channel-non-off state are partially similar and partially different.

[0202] There are three cells C1, C2, and C3 in the optimized cluster.

[0203] C1 has two strategies: channel shutdown 1 / 4 and non-channel shutdown;

[0204] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0205] C3 has 1 strategy: non-channel shutdown.

[0206] Step S1: Calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0207] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell.

[0208] First, divide the grid into two dimensions: HDOA uses HorizontalBeamGridSpace = 60, and VDOA uses VerticalBeamGridSpace = 20. By calculating the percentage of each grid, we can obtain the distribution of user terminals in different channel shutdown states in each cell.

[0209] The distribution of user terminals with non-channel shutdown in C1 is shown in Table 8:

[0210] Table 8

[0211] The distribution of user terminals with 1 / 4 of the C1 channel turned off is shown in Table 9:

[0212] Table 9

[0213] Step S2, calculate the Euclidean distance according to the above formula (1) to determine the similarity.

[0214] Assume that the similarity distance threshold is 0.3. If 0.41>0.3, it means that the user terminal distribution when the C1 channel is not shut down is different from the user terminal distribution when the C1 channel is shut down 1 / 4.

[0215] Calculate in the same way:

[0216] The Euclidean distance between the user terminal distribution when 1 / 4 of the C2 channels are shut down and the user terminal distribution when the C2 channels are not shut down is 0.25, which is less than 0.3, indicating that the user terminal distribution when 1 / 4 of the C2 channels are shut down is similar to the user terminal distribution when the channels are not shut down.

[0217] The Euclidean distance between the user terminal distribution when 1 / 2 of the C2 channels are shut down and the user terminal distribution when the C2 channels are not shut down is 0.4, which is greater than the threshold of 0.3, indicating that the user terminal distribution when 1 / 2 of the C2 channels are shut down is different from the user terminal distribution when the channels are not shut down.

[0218] The C3 cell has only one state, so there is no need to calculate the distance.

[0219] In summary, the distribution of user terminals according to channel shutdown strategies is as follows:

[0220] C1 user terminal distribution: 1 / 4 channels are shut down, non-channels are shut down

[0221] C2 user terminal distribution: channel shutdown 1 / 4 and non-channel shutdown, channel shutdown 1 / 2

[0222] C3 user terminal distribution: non-channel shutdown

[0223] Step S3, convert RSRP using formula (2) in step S602

[0224] According to step S602, the RSRP data reported when the C2 channel is turned off 1 / 4 is processed;

[0225] Step S4, by using step S507 to calculate the optimal antenna weights of C1, C2, and C3 in each channel-off state and non-channel-off state, as shown in Table 10, where the channel-off 1 / 4 of C2 is replaced by the corresponding non-channel-off form.

[0226] Table 10

[0227] Step S5: querying a weight mapping table of channel shutoff and non-channel shutoff.

[0228] Find the weight of the channel corresponding to W12 of C2 when it is turned off 1 / 4, and the weight of the channel corresponding to W32 when it is turned off 1 / 4 is Wj

[0229] The weights of each cell after updating Table 10 are shown in Table 11:

[0230] Table 11

[0231] Step S6: Based on the weight mapping table obtained in step S5, the optimal antenna weights of C1, C2, and C3 in each channel off state and non-channel off state are enabled in the network element, so that when the cell switches to a different state combination, the corresponding weights are automatically enabled.

[0232] Through the above-mentioned embodiments of the present invention, the loopholes in the related technology for optimizing coverage in the channel-off state are made up. By identifying the similarities between the user distribution in the channel-off state and the non-energy-saving state, better antenna weights are optimized for each state and combination, so that the coverage in the channel-off state is more in line with the user distribution at that time, thereby improving performance indicators and user experience.

[0233] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0234] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0235] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0236] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0237] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0238] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0239] The foregoing description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be included within the scope of protection of the present disclosure.

Claims

1. A method for determining antenna weights, comprising: Identify the distribution of user terminals in cells under different channel states based on user terminal data and channel state information; Based on the user terminal distribution, optimal antenna weights under the different channel states are determined.

2. The method according to claim 1, wherein: The channel status information includes: a channel shutdown state and a non-channel shutdown state; the channel shutdown state includes: a plurality of channel shutdown states after channel shutdown according to different channel shutdown ratios, and the channel shutdown ratio is the ratio of the number of shutdown channels to the total number of antenna channels.

3. The method according to claim 2, wherein: Identify the distribution of user terminals in the cell under different channel conditions, including: For any cell in any optimization cluster, group the user terminal data in the cell according to different channel states, wherein the optimization cluster is composed of a plurality of cells whose area overlap between cells meets a threshold condition, and each cell in the optimization cluster includes at least one channel state; Based on the horizontal direction of arrival HDOA and the vertical direction of arrival VDOA of the cell, rasterizing the user terminal data in each group to obtain rasterized data corresponding to the different channel states; Respectively obtaining the similarity of the rasterized data between different channel-off states and the non-channel-off states; Based on the similarity, the distribution of user terminals in different channel states in the cell is identified.

4. The method according to claim 3, wherein: The distribution of user terminals in a cell under different channel off states includes one of the following: All the channel-off states are consistent with the distribution of user terminals in the non-channel-off states; All the channel-off states are inconsistent with the distribution of user terminals in the non-channel-off states; The distribution of some of the user terminals in the channel-off state is consistent with that in the non-channel-off state; The distribution of some of the user terminals in the channel-off state and the user terminals in the non-channel-off state is inconsistent.

5. The method according to claim 4, wherein: In a case where the distribution of user terminals in all the channel-off states is consistent with that in the non-channel-off state, determining optimal antenna weights in the different channel states includes: For any of the channel off states in the cell, converting the reference signal received power RSRP data collected in the channel off state into first RSRP data in a non-channel off state; Based on the first RSRP data and the gain table, second RSRP data under multiple different antenna weights are estimated; The first RSRP data and the second RSRP data are compared, and the antenna weight corresponding to the maximum RSRP data is selected as the optimal antenna weight in the channel off state.

6. The method according to claim 4, wherein: In a case where the distribution of user terminals in all the channel-off states is inconsistent with that in the non-channel-off state, determining the optimal antenna weights in the different channel states includes: For any of the channel off states in the cell, based on the third RSRP data in the channel off state and the gain table, fourth RSRP data under a plurality of different antenna weights are estimated; The third RSRP data and the fourth RSRP data are compared, and the antenna weight corresponding to the maximum RSRP data is selected as the optimal antenna weight in the channel off state.

7. The method according to claim 4, wherein: In a case where the distribution of some of the user terminals in the channel-off state is consistent with that in the non-channel-off state, determining the optimal antenna weights in the different channel states includes: In the cell, for any of the channel-off states in which the user terminal distribution is consistent with the non-channel-off state, RSRP data collected in the channel-off state is converted into fifth RSRP data of the non-channel-off state; and based on the fifth RSRP data and the gain table, sixth RSRP data under multiple different antenna weights are estimated; the fifth RSRP data and the sixth RSRP data are compared, and the antenna weight corresponding to the maximum RSRP data is selected as the optimal antenna weight in the channel-off state.

8. The method according to claim 4, wherein: In the case where the distribution of user terminals in the channel-off state and the non-channel-off state is inconsistent, determining the optimal antenna weights in the different channel states includes: For any of the channel-off states in which the user terminal distribution is inconsistent with the non-channel-off state, based on the seventh RSRP data and the gain table in the channel-off state, the eighth RSRP data under multiple different antenna weights is estimated; and the seventh RSRP data and the eighth RSRP data are compared, and the antenna weight corresponding to the maximum RSRP data is selected as the optimal antenna weight in the channel-off state.

9. The method according to any one of claims 5 to 8, wherein: Determining optimal antenna weights under the different channel states, the method further includes: The antenna weight corresponding to the RSRP data collected in the non-channel shutoff state is determined as the optimal antenna weight in the non-channel shutoff state.

10. The method according to claim 1, wherein: After determining the optimal antenna weights in the different channel off states, the method further includes: According to the current channel state of the cell, the antenna weight of the cell is switched to an optimal antenna value corresponding to the current channel state.

11. An antenna weight determination device, comprising: An identification module, configured to identify the distribution of user terminals in a cell under different channel states based on user terminal data and channel state information; A determination module is configured to determine optimal antenna weights under the different channel states based on the user terminal distribution.

12. A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 10 when executed by a processor.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 10 are implemented.

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