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

Abstract

The embodiment of the invention provides an antenna weight determination method and device, a storage medium and an electronic device. The method comprises the following steps: identifying user terminal distribution in a cell under different channel states according to user terminal data and channel state information; and based on the distribution of the user terminals, determining optimal antenna weights in the different channel states. According to the embodiment of the invention, the user terminal distribution in the cell under different channel states is identified according to the user terminal data and the channel state information, so that the optimal antenna weight under the different channel states is determined based on the user terminal distribution. Therefore, in the process of determining the optimal antenna weight under different channel states, the channel state information can be introduced, so that the problem that an existing antenna weight adjustment scheme in the related technology cannot be suitable for a channel turn-off energy-saving opening scene can be solved, and the effect of determining the accuracy of the optimal antenna weight is achieved.

Description

Technical Field

[0001] The embodiments of the present invention 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] In order to reduce energy consumption and waste, some intelligent energy-saving solutions are usually adopted in some antenna usage scenarios. For example, some channels of the antenna are shut down to achieve channel shutdown energy saving. However, when the channel is shut down but the antenna weight is not modified, the coverage of the antenna will change. The change in coverage will affect the normal communication between various network devices. Therefore, in order to ensure the stability of the communication environment, the antenna needs to have the optimal network coverage when the shutdown state of the channel changes.

[0003] It is known that the preset antenna weights cannot cope with various coverage scenarios. Therefore, in order to cope with various coverage scenarios and optimize the coverage effect, the following methods are often used in related technologies: collect MR (Measurement Report) data from user terminals, estimate the coverage under each antenna weight, and then combine AI (Artificial Intelligence) optimization algorithm to select the antenna weight that can achieve the best coverage. However, this optimization process cannot be applied to the scenario where the channel shutdown energy-saving function is turned on, because the key technology used by the algorithm is to estimate the coverage effect under other weights based on the gain of the antenna under each weight. However, when the channel is shut down, the original antenna gain has changed, and the same antenna weight forms different coverage patterns in the channel shutdown and non-shutdown scenarios. There is a deviation in the estimated coverage effect under other weights. Therefore, when antenna weight optimization and channel shutdown energy saving are turned on at the same time, the antenna weight optimized by the AI ​​optimization algorithm may not be the optimal result, thereby affecting the network coverage effect. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, storage medium and electronic device for determining antenna weights, so as to at least solve the problem that the existing antenna weight adjustment scheme in the related art cannot be applied to the channel shut-down energy-saving start-up scenario.

[0005] According to one embodiment of the present invention, 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.

[0006] According to another embodiment of the present invention, there is provided an antenna weight determination device, comprising: an identification module, which identifies 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, which determines the optimal antenna weights under the different channel states based on the user terminal distribution.

[0007] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0008] According to yet another embodiment of the present invention, there is provided 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 execute the steps in any one of the above method embodiments.

[0009] Through the above-mentioned embodiments of the present invention, the distribution of user terminals in a cell under different channel states is identified based on user terminal data and channel state information, thereby determining the optimal antenna weights under the different channel states based on the user terminal distribution. It can be seen that in the process of determining the optimal antenna weights under different channel states, the channel state information can be introduced, so that the problem that the existing antenna weight adjustment scheme in the related art cannot be applied to the channel shutdown energy saving start scenario can be solved, and the accuracy effect of determining the optimal antenna weights can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a hardware structure block diagram of a network device for executing an antenna weight determination method according to an embodiment of the present invention;

[0011] Figure 2 is a schematic diagram of a network architecture for running a method for determining antenna weights according to an embodiment of the present invention;

[0012] Figure 3 is a flow chart of a method for determining antenna weights according to an embodiment of the present invention;

[0013] Figure 4 is a structural block diagram of an antenna weight determination device according to an embodiment of the present invention;

[0014] Figure 5 is a flow chart of a method for self-adjusting antenna weights according to an embodiment of the present invention;

[0015] Figure 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 invention;

[0016] Figure 7It 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 invention. DETAILED DESCRIPTION

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

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 running on a network device as an example, Figure 1 1 is a hardware structure block diagram of a network device for executing the antenna weight determination method according to an embodiment of the present invention. Figure 1 As shown, the network device may include one or more ( Figure 1 Only one is shown in the figure) a processor 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 network device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above network device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0020] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the antenna weight determination method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to a network device via a network. Examples of the above-mentioned network 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. The specific example of the above network may include a wireless network provided by a communication provider of the network device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

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

[0023] A network element is used to collect user terminal data and cell channel shutdown status information; the user terminal data is the signal strength and relative position of the cell measured by the terminal, which is data used to characterize 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] Network element management equipment, used to collect and store user terminal data and cell channel status information, and start user terminal distribution identification after data collection is completed;

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

[0026] For cells where the distribution of channel-shutdown and non-channel-shutdown user terminals is consistent, the network element management device automatically performs data conversion, converts the user terminal data corresponding to the channel-shutdown scenario into the corresponding non-channel-shutdown scenario, so as to unify the user terminal data of different scenarios and solve the problem of change in antenna gain after channel shutdown. Direct use of the original user terminal data for calculation will lead to inaccurate input data and deviation in calculation results. The converted user data is optimized together with the user data in the non-channel-shutdown state to optimize a set of optimal antenna weights, which are suitable for non-shutdown scenarios, and outputs the optimal antenna weights in the non-channel-shutdown scenario. According to the mapping relationship between the channel-shutdown weights and the non-channel-shutdown weights under the same coverage pattern of the network element management device, a set of appropriate 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 of changes in the antenna coverage range due to not changing the weights 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 of the channel shutdown scenario match its user terminal distribution, and the weights of the non-channel shutdown scenario match its user terminal distribution, so as to solve the scenario where the user terminal distribution changes after 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 network device or network architecture is provided. Figure 3 is a flow chart of a method for determining antenna weights according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

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

[0033] The channel shutoff state includes: a plurality of channel shutoff states after shutting down the 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 channels of the antenna.

[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 the 5G network environment, the current antenna types mainly include 64 channels, 32 channels, 16 channels, 8 channels, and 4 channels. For 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 are also included: shutting down one eighth of the channel, shutting down one sixteenth of the channel, and shutting down one thirty-second of the channel.

[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, so as to determine the antenna weights based on the optimization cluster.

[0040] In one embodiment, the distribution of user terminals in a cell under different channel states is identified, including: for any cell in any of the optimization 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; 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; and based on the similarity, identifying the distribution of user terminals in the cell under different channel states.

[0041] Among them, the distribution of user terminals in the cell under different channel shut-off states includes one of the following: the distribution of user terminals in all the channel shut-off states is consistent with that in the non-channel shut-off state; the distribution of user terminals in all the channel shut-off states is inconsistent with that in the non-channel shut-off state; the distribution of user terminals in some of the channel shut-off states is consistent with that in the non-channel shut-off state; the distribution of user terminals in some of the channel shut-off states is inconsistent with that in the non-channel shut-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 of the non-channel-off state; estimating second RSRP data under multiple different antenna weights based on the first RSRP data and a gain table; comparing the first RSRP data with the second RSRP data, and selecting the antenna weight corresponding to the largest 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 optimal antenna weights under the different channel states are determined, and the method further includes: 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 user terminal distribution in the cell under different channel states is identified based on the user terminal data and the channel state information, so as to determine the optimal antenna weight under the different channel states based on the user terminal distribution. It can be seen that in the process of determining the optimal antenna weight under different channel states, the channel state information can be introduced. Therefore, the problem that the existing antenna weight adjustment scheme in the related art cannot be applied to the channel shutdown energy saving start scenario can be solved, and the accuracy effect of determining the optimal antenna weight can be achieved.

[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 a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, 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, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0051] In this embodiment, an antenna weight determination device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0052] Figure 4 is a structural block diagram of an antenna weight determination device according to an embodiment of the present invention. Figure 4 As shown, the device is used to run the antenna weight determination method in any of the above embodiments, including: an identification module 10 and a determination module 20.

[0053] An identification module 10 identifies the distribution of user terminals in a cell under different channel states according to 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: a plurality of channel shutdown states after the channels are shut down according to different channel shutdown ratios, and the channel shutdown ratio is the ratio of the number of shut-down channels to the total number of antenna channels.

[0055] The determination module 20 determines 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 optimization cluster 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;

[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 acquisition unit, used for respectively acquiring the similarity of the rasterized data between the different channel-off states and the non-channel-off states;

[0060] The identification unit is used 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: the distribution of user terminals in all the channel-off states is consistent with that in the non-channel-off states; the distribution of user terminals in all the channel-off states is inconsistent with that in the non-channel-off states; the distribution of user terminals in some of the channel-off states is consistent with that in the non-channel-off states; the distribution of user terminals in some of the channel-off states is inconsistent with that in the non-channel-off states.

[0062] In an exemplary embodiment, when all the channel-off states are in the same distribution as the user terminals in the non-channel-off state, the determination module 20 includes:

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

[0064] A first estimation unit, 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 used to compare the first RSRP data with the second RSRP data, and select the antenna weight corresponding to the maximum 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 states, the determining module 20 includes:

[0067] A second estimation 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 a gain table;

[0068] The second comparison unit is used to compare the third RSRP data with the fourth RSRP data, and select the antenna weight corresponding to the maximum 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 determination module 20 includes:

[0070] A second conversion unit is used 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 estimation 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 comparison 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 that in the non-channel-off state, the determination module 20 includes:

[0074] a fourth estimation 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 and the gain table in the channel-off state;

[0075] The fourth comparing unit is used to compare the seventh RSRP data with the eighth RSRP data, and select the antenna weight corresponding to the maximum 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 by 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 the understanding of the technical solution provided by the present invention, a detailed description will be given below in conjunction with embodiments of specific scenarios.

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

[0080] The gain table is built into the processing system of the network management device, and includes: a gain table of each antenna in a non-shutdown state and a gain table in a 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 a non-shutdown state and a gain table in a 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 which 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 part of the antenna weights.

[0083] Table 1

[0084]

[0085] Each type of antenna has multiple sets of table formats as shown in Table 1, for example:

[0086] For a 64-channel antenna, it includes: a 64T non-shutdown weight gain table, a 64T shutoff 1 / 4 weight gain table, a 64T shutoff 1 / 2 weight gain table, and a 64T shutoff 3 / 4 weight gain table.

[0087] For a 32-channel antenna, it includes: a 32T non-shutdown weight gain table, a 32T shutoff 1 / 4 weight gain table, a 32T shutoff 1 / 2 weight gain table, and a 32T shutoff 3 / 4 weight gain table.

[0088] 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.

[0089] The corresponding relationship between the non-channel shutoff weights of each antenna and the weights when the channel is shut off is shown in Table 2, which is built into the system of the network management device. For the same coverage pattern, the antenna weights and the number of antennas corresponding to different numbers of channels are fixed, and the table is fixed.

[0090] Table 2

[0091] Non-shutdown weight Off 1 / 4 weight Off 1 / 2 weight Off 3 / 4 weight W1 W11 W12 W13 W1 W21 W22 W23 … … … …

[0092] In this embodiment, there are as many types of the above-mentioned corresponding relationships as there are groups of weights.

[0093] 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, and SubBeam is a sub-beam:

[0094] SubBeam1 azimuth='-44', tilt='-1', beamWidthH='10', beamWidthV='6';

[0095] SubBeam2 azimuth='-31', tilt='-1', beamWidthH='10', beamWidthV='6';

[0096] SubBeam3 azimuth='-18', tilt='-1', beamWidthH='10', beamWidthV='6';

[0097] SubBeam4 azimuth='-6', tilt='-1', beamWidthH='10', beamWidthV='6';

[0098] SubBeam5 azimuth='6', tilt='-1', beamWidthH='10', beamWidthV='6';

[0099] SubBeam6 azimuth='18', tilt='-1', beamWidthH='10', beamWidthV='6';

[0100] SubBeam7 azimuth='31', tilt='-1', beamWidthH='10', beamWidthV='6';

[0101] SubBeam8 azimuth='44', tilt='-1', beamWidthH='10', beamWidthV='6';

[0102] The embodiment of the present invention provides a method for automatically adjusting antenna weights, through which the characteristics of user terminal distribution in a channel-off energy-saving state and user terminal distribution in a non-channel-off state are automatically identified, so that different optimization strategies are automatically adapted for user terminal distributions in different states, so that wireless coverage is consistent with user terminal distribution to achieve optimal coverage, and the problem that channel-off energy-saving affects the optimization effect of antenna weights can be solved.

[0103] This method can be applied to wireless network optimization scenarios, and can optimize the wireless coverage effect when the channel is shut down to save energy or when the energy-saving state affects the antenna gain change.

[0104] Figure 5 is a flow chart of a method for self-adjusting antenna weights according to an embodiment of the present invention. The method can be applied to channel shutdown scenarios, such as Figure 5 As shown, the method comprises the following steps:

[0105] Step S501, select an optimization target range and start data collection of network elements.

[0106] Step S502: The network element collects user terminal data and channel status information.

[0107] Among them, the user terminal data is the signal strength and the relative position of the cell measured by the user terminal, which is data used to characterize the distribution of the user terminal, including: RSRP, HDOA, VDOA of the cell where the user terminal is located, and RSRP, HDOA and VDOA information of the neighboring cells of the cell measured by the user terminal.

[0108] The channel status information is the antenna channel shutdown status when the user terminal reports the user terminal data.

[0109] Step S503: the network element management device collects and stores user terminal equipment data and cell channel shutdown status information.

[0110] Step S504: using an optimization clustering algorithm, groups cells with similar relationships into an optimization cluster, and performs adjustment and optimization in units of the optimization cluster.

[0111] Since antenna weight adjustment may affect other cells, thus causing overlapping coverage and over-coverage, it is necessary to group cells first and adjust the cells in the group together to take into account the impact between cells.

[0112] Specifically, cells whose area overlap between cells meets a threshold condition are divided into an optimization cluster.

[0113] 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 user terminal distribution of non-channel shutdown. 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.

[0114] The main basis for coverage optimization is the user terminal distribution. The purpose of optimization is to make the antenna weight coverage consistent with the user terminal distribution to achieve the best coverage effect. In this embodiment, the user terminal distribution self-identification can identify the distribution consistency in the channel shutdown and non-shutdown states, and different optimization strategies are selected based on this feature.

[0115] Specifically, taking the optimized cluster as a unit, self-identification includes the following steps:

[0116] Step S5051, grouping the user terminal data of each cell according to different channel shutdown states, for example: grouping according to channel shutdown 1 / 4, through shutdown 1 / 2, channel shutdown 3 / 4 and not shut down; wherein, the channel shutdown strategies of each cell may be different, there may be only 2 or only 1.

[0117] Step S5052: divide the data of each group into two-dimensional grids according to HDOA and VDOA, and calculate the proportion of MR user data in each grid. The MR user data includes: RSRP, HDOA and VDOA.

[0118] Specifically, HDOA uses the step size of HorizontalBeamGridSpace, and VDOA uses the step size of VerticalBeamGridSpace to perform gridding, and the proportion of MRs in each grid is counted.

[0119] 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: X1n = [x11, x12, x13, ... x1n] shuts down 1 / 4 channels

[0120] X2n=[x21,x22,x23,……x2n] shut down 1 / 2 channels

[0121] X3n=[x31,x32,x33,……x3n] shut down 3 / 4 channels

[0122] X0n=[x01,x02,x03,……x0n] non-channel shutdown

[0123] Step S5053, calculating the similarity between the rasterized data when each channel is turned off and the data when it is not turned off;

[0124] For example, by calculating the Euclidean distance between two vectors, the closer the distance, the greater the similarity. Set a distance threshold to control the calculation of similarity to cope with different usage scenarios. The calculation formula of the Euclidean distance is shown in formula (1).

[0125]

[0126] Step S506, when the distribution of user terminals in 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 to take effect.

[0127] The algorithm idea of ​​this step optimization strategy is:

[0128] Since the antenna gain will change after the channel is shut down, the user terminal data in the non-channel shutoff 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 shutoff state needs to be converted into the corresponding user terminal data in the non-channel shutoff state, and the antenna weight optimization is performed together with the data in the non-channel shutoff state reported by the terminal user to optimize a weight suitable for the coverage range of the non-channel shutoff scenario. Then, the weight under the channel shutoff is matched according to the weight relationship between channel shutoff and non-channel shutoff under the same coverage range (i.e., Table 1 and Table 2), and it takes effect automatically.

[0129] Specifically, Figure 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 invention. Figure 6 As shown, the process includes the following steps:

[0130] Step S601, filtering the user terminal data in the channel off state, so as to group the user terminal data of the cell according to the different channel off states of each cell;

[0131] Step S602, the RSRP data in the channel-off state is processed and converted into the corresponding data in the non-channel-off state to obtain RSRP CDF50, wherein the conversion formula is as shown in formula (2):

[0132] RSRP=RSRP s +delta-RSRP(VDOA, HDOA) formula (2)

[0133] Among them, RSRP s : The RSRP value collected when the channel is turned off;

[0134] delta-RSRP (VDOA, HDOA): is the difference between the antenna gain of the non-channel-off SSB weight of the cell and the antenna gain of the channel-off SSB (Single Side Band) weight corresponding to the sample.

[0135] Step S603, using an intelligent optimization algorithm to calculate the optimal antenna weights, determine the maximum RSRP CDF50, the data source is the RSRP data in the channel-off state and the RSRP data in the non-channel-off state converted in step S601.

[0136] Specifically, in this embodiment, the ant colony algorithm is used to calculate the optimal antenna weights. The input of the algorithm is the RSRP data in the channel off state after the data processing in step S602 and the RSRP data reported in the non-channel off state. The optimization goal is to optimize the RSRP cdf50, that is, the coverage is optimal. The following optimization is performed in units of optimization clusters.

[0137] Specifically, the optimization algorithm includes the following steps:

[0138] Step 6031, calculate RSRP CDF50 under the currently configured antenna weights, the data source is the data in the channel-off state and the RSRP data in the non-channel-off state converted according to S602.

[0139] 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 group of weights. The estimation formula is as follows:

[0140] RSRP wi =RSRP+AntGain i -AntGain formula (3)

[0141] Among them, RSRP: RSRP output by S602 and RSRP reported in the non-channel off state;

[0142] RSRP wi : Estimated RSRP when the weight is wi;

[0143] AntGain i : Look up the gain table to obtain the corresponding gain under the wi weight (HDOA, VDOA);

[0144] AntGain: The gain corresponding to (HDOA, VDOA) under the weight w obtained by looking up the gain table;

[0145] Step 6033 calculates RSRP cdf50 and outputs the weighted combination with the largest RSRP CDF50 in this iteration.

[0146] Specifically, the maximum RSRP CDF 50 is selected from the RSRP CDF50 under the currently configured antenna weights and the RSRPCDF50 under each group of weights through the ant colony algorithm.

[0147] 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.

[0148] Step S604, according to the optimal weight combination output in S603, and according to Table 1 and Table 2, the antenna weights of channel shutoff 1 / 4, channel shutoff 1 / 2, and channel shutoff 3 / 4 are obtained.

[0149] 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.

[0150] Step S507, when the distribution of user terminals in channel-off and non-channel-off conditions is inconsistent, use the data collected in non-channel-off conditions to optimize the antenna weights in non-channel-off conditions; then use the data in channel-off conditions to optimize different antenna weights for each channel-off strategy.

[0151] The algorithm idea of ​​this step optimization strategy is:

[0152] 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.

[0153] Figure 7 4 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 invention. Figure 7 As shown, the process includes the following steps:

[0154] Step S701, filtering the user terminal data in the channel off state;

[0155] Step S702: Calculate the optimal weights of cells in the optimization cluster under different channel shutdown states.

[0156] For example: there are 3 cells C1, C2, and C3 in the optimization cluster. C1 has 2 strategies (shutdown 1 / 4 and not shut down), C2 has 3 strategies (shutdown 1 / 4, shutdown 1 / 2, and not shut down), and C3 has 1 strategy (not shut down). Then calculate the cell weights with the smallest combination of various strategies respectively.

[0157] 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 step S603;

[0158] The weights of other combinations are calculated in the above manner, for a total of 6 combinations, and 6 sets of weights are calculated.

[0159] Step S703, calculating the weight combinations of other optimization clusters in the above manner;

[0160] 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.

[0161] Scenario Example 1:

[0162] 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.

[0163] There are three cells C1, C2, and C3 in the optimized cluster;

[0164] C1 has two strategies: channel shutdown 1 / 4, non-channel shutdown;

[0165] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0166] C3 has 1 strategy: non-channel shutdown.

[0167] Step S1, calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0168] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell;

[0169] First, divide the two-dimensional grid, HDOA with HorizontalBeamGridSpace = 60, VDOA with VerticalBeamGridSpace = 20. Count the proportions in each grid respectively, and you can get the distribution of user terminals in different channel shutdown states in each cell.

[0170] The distribution of user terminals with non-channel shutdown of C1 is shown in Table 3:

[0171] Table 3

[0172] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.4 0.1 VDOA[0,20] 0.35 0.15

[0173] The distribution of user terminals with 1 / 4 of the C1 channel turned off is shown in Table 4:

[0174] Table 4

[0175] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.45 0.15 VDOA[0,20] 0.3 0.1

[0176] Step S2, calculating the Euclidean distance according to the above formula (1) to determine the similarity.

[0177]

[0178] Assuming that the similarity distance threshold is 0.3 and 0.1<0.3, it means that the user terminal distribution when the C1 non-channel is turned off is the same as the user terminal distribution when the C1 channel is turned off 1 / 4.

[0179] Calculate in the same way:

[0180] The Euclidean distance between the user terminal distribution when 1 / 4 of the C2 channel is shut down and the user terminal distribution when the C2 channel is not shut down is 0.15.

[0181] The Euclidean distance between the user terminal distribution when the C2 channel is 1 / 2 closed and the user terminal distribution when the C2 channel is not closed is 0.2, both of which are less than the threshold of 0.3. This means that the user terminal distribution when the C2 channel is not closed is the same as the user terminal distribution when the C2 channel is closed.

[0182] In the non-channel shutdown state, the C3 cell has only one state, and the distance does not need to be calculated.

[0183] Step S3, convert RSRP by formula (2) in step S602

[0184] Convert the RSRP when the C1 channel is shut down 1 / 4 to the RSRP in the non-shutdown state;

[0185] Convert the RSRP when the C2 channel is shut down 1 / 4 to the RSRP in the non-shutdown state;

[0186] Convert the RSRP when the C2 channel is shut down 1 / 2 to the RSRP in the non-shutdown state;

[0187] Step S4, through steps S603-S604, calculate the optimal weights of C1, C2, and C3 in the non-shutdown state: C1 optimal w1, C2 optimal w2, C3 optimal w3;

[0188] 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; the weight w22 corresponding to w2 when the channel is closed 1 / 2.

[0189] That is, the weight of cell C1 is w1 when it is in the non-channel-off state, and the weight is w11 when it is 1 / 4 off;

[0190] The weight of cell C2 in the non-channel-off state is w2, the weight when 1 / 4 is off is w21, and the weight when 1 / 2 is off is w22;

[0191] The optimal weight of C3 is w3.

[0192] In an exemplary embodiment, the number of divided grids is greater than 8.

[0193] Scenario Example 2:

[0194] 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-non-off state.

[0195] There are three cells C1, C2, and C3 in the optimized cluster;

[0196] C1 has two strategies: channel shutdown 1 / 4, non-channel shutdown;

[0197] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0198] C3 has 1 strategy: non-channel shutdown.

[0199] Step S1, calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0200] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell;

[0201] First, divide the two-dimensional grid, HDOA with HorizontalBeamGridSpace = 60, VDOA with VerticalBeamGridSpace = 20. Count the proportions in each grid respectively, and you can get the distribution of user terminals in different channel shutdown states in each cell.

[0202] The distribution of user terminals with non-channel shutdown of C1 is shown in Table 5:

[0203] Table 5

[0204] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.4 0.1 VDOA[0,20] 0.35 0.15

[0205] The distribution of user terminals with 1 / 4 of the C1 channel turned off is shown in Table 6:

[0206] Table 6

[0207] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.1 0.2 VDOA[0,20] 0.6 0.1

[0208] Step S2, calculate the Euclidean distance according to the above formula (1) to determine the similarity

[0209]

[0210] Assume that the similarity distance threshold is 0.3, 0.41>0.3, which 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.

[0211] Calculate in the same way:

[0212] The Euclidean distance between the user terminal distribution when 1 / 4 of the C2 channel is shut down and the user terminal distribution when the C2 channel is not shut down is 0.35.

[0213] The Euclidean distance between the user terminal distribution when the C2 channel is shut down 1 / 2 and the user terminal distribution when the C2 channel is not shut down is 0.3, both of which are greater than the threshold 0.3, which means that the user terminal distribution when the C2 channel is not shut down is different from the user terminal distribution when the C2 channel is shut down.

[0214] In the non-channel shutdown state, the C3 cell has only one state, and the distance does not need to be calculated.

[0215] Step S3, through step S507, calculate the optimal weights of C1, C2, and C3 when each channel is turned off, as shown in Table 7:

[0216] Table 7

[0217] Cell channel shutdown status C1 weight C2 weight C3 weight C1 is closed 1 / 4, C2 is closed 1 / 4, C3 is not closed W11 W12 W13 C1 is closed 1 / 4, C2 is closed 1 / 2, C3 is not closed W21 W22 W23 C1 is closed 1 / 4, C2 is not closed, C3 is not closed W31 W32 W33 C1 is not closed, C2 is not closed, C3 is not closed W41 W42 W43 C1 is not closed, C2 is 1 / 2 closed, C3 is not closed W51 W52 W53 C1 is not closed, C2 is 1 / 4 closed, C3 is not closed W61 W62 W63

[0218] 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 when the cell switches to a different state combination, the corresponding weights are automatically validated.

[0219] Scenario Example 3:

[0220] 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 in the optimization cluster are partially similar and partially dissimilar.

[0221] There are three cells C1, C2, and C3 in the optimized cluster.

[0222] C1 has two strategies: channel shutdown 1 / 4, non-channel shutdown;

[0223] C2 has three strategies: channel shutdown 1 / 4, channel shutdown 1 / 2, and non-channel shutdown;

[0224] C3 has 1 strategy: non-channel shutdown.

[0225] Step S1, calculate the distribution of user terminals in each cell under different channel shutdown states according to step S505:

[0226] For example, using 1000 MR data, statistics are collected on the distribution of user terminals in different channel shutdown states in each cell;

[0227] First, divide the two-dimensional grid, HDOA with HorizontalBeamGridSpace = 60, VDOA with VerticalBeamGridSpace = 20. Count the proportions in each grid respectively, and you can get the distribution of user terminals in different channel shutdown states in each cell.

[0228] The distribution of user terminals with non-channel shutdown of C1 is shown in Table 8:

[0229] Table 8

[0230] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.4 0.1 VDOA[0,20] 0.35 0.15

[0231] The distribution of user terminals with 1 / 4 of the C1 channel turned off is shown in Table 9:

[0232] Table 9

[0233] HDOA[-60,-1] HDOA[0,60] VDOA[-20,-1] 0.1 0.2 VDOA[0,20] 0.6 0.1

[0234] Step S2, calculating the Euclidean distance according to the above formula (1) to determine the similarity.

[0235]

[0236] Assume that the similarity distance threshold is 0.3, 0.41>0.3, which 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.

[0237] Calculate in the same way:

[0238] The Euclidean distance between the user terminal distribution when the C2 channel is shut down 1 / 4 and the user terminal distribution when the C2 channel is not shut down is 0.25, which is less than 0.3, indicating that the user terminal distribution when the C2 channel is shut down 1 / 4 is similar to the user terminal distribution when the channel is not shut down.

[0239] The Euclidean distance between the user terminal distribution when the C2 channel is shut down 1 / 2 and the user terminal distribution when the C2 channel is not shut down is 0.4, which is greater than the threshold 0.3, indicating that the user terminal distribution when the C2 channel is shut down 1 / 2 is different from the user terminal distribution when the C2 channel is not shut down.

[0240] The C3 cell has only one state, so there is no need to calculate the distance.

[0241] In summary, the distribution of user terminals according to the channel shutdown strategy is as follows:

[0242] C1 user terminal distribution: 1 / 4 channels are shut down, non-channels are shut down

[0243] C2 user terminal distribution: channel shutdown 1 / 4 and non-channel shutdown, channel shutdown 1 / 2

[0244] C3 user terminal distribution: non-channel shutdown

[0245] Step S3, convert RSRP by formula (2) in step S602

[0246] According to step S602, the RSRP data reported when the C2 channel is turned off 1 / 4 is processed;

[0247] Step S4, by using step S507 to calculate the optimal antenna weights of C1, C2, and C3 in each channel-off and non-channel-off state, as shown in Table 10, wherein the channel-off 1 / 4 of C2 is replaced by the corresponding non-channel-off form.

[0248] Table 10

[0249] Cell channel shutdown status C1 weight C2 weight C3 weight C1 is closed 1 / 4, C2 is closed 1 / 4 and not closed, C3 is not closed W11 W12 W13 C1 is closed 1 / 4, C2 is closed 1 / 2, C3 is not closed W21 W22 W23 C1 is not closed, C2 is not closed and closed 1 / 4, C3 is not closed W31 W32 W33 C1 is not closed, C2 is 1 / 2 closed, C3 is not closed W41 W42 W43

[0250] Step S5, querying a weight mapping table of channel shutoff and non-channel shutoff.

[0251] 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

[0252] The weights of each cell after updating Table 10 are shown in Table 11:

[0253] Table 11

[0254] Cell channel shutdown status C1 weight C2 weight C3 weight C1 is closed 1 / 4, C2 is not closed, C3 is not closed W11 W12 W13 C1 is closed 1 / 4, C2 is closed 1 / 4, C3 is not closed W11 Wi W13 C1 is closed 1 / 4, C2 is closed 1 / 2, C3 is not closed W21 W22 W23 C1 is not closed, C2 is not closed, C3 is not closed W31 W32 W33 C1 is not closed, C2 is 1 / 4 closed, C3 is not closed W31 Wj W33 C1 is not closed, C2 is 1 / 2 closed, C3 is not closed W41 W42 W43

[0255] 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 effective in the network element, so that when the cell switches to a different state combination, the corresponding weights are automatically effective.

[0256] Through the above-mentioned embodiments of the present invention, the loophole of the related technology in optimizing coverage when the channel is turned off is made up for. By identifying the similarity between the user distribution under the channel-off state and the non-energy-saving state, better antenna weights are optimized for each state and combination, so that the coverage under the channel-off state is more in line with the user distribution at that time, thereby improving performance indicators and user experience.

[0257] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0258] 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.

[0259] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0260] 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.

[0261] 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 herein.

[0262] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that 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 executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0263] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining antenna weights, characterized in that: include: 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, characterized in that in, 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, characterized in that 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, characterized in that in, 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that: 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, characterized in that 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, characterized in that: include: An identification module, which identifies the distribution of user terminals in cells under different channel states according to user terminal data and channel state information; A determination module determines optimal antenna weights under the different channel states based on the user terminal distribution.

12. A computer-readable storage medium, characterized in that: 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, characterized in that: 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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