Beam weight determination method, base station and storage medium

Abstract

The embodiment of the invention provides a beam weight determination method, a base station and a storage medium, and belongs to the technical field of communication. The beam weight determination method comprises the following steps: positioning a user terminal accessed to a base station cell to obtain a current coordinate of the user terminal; according to the current coordinates, a preset fingerprint database is inquired, the current corresponding beam weight of the user terminal is determined, the coverage area of the base station cell is divided into a plurality of target areas, and the fingerprint database comprises the mapping relation between the reference coordinates of the target areas and the beam weight. According to the technical scheme of the embodiment of the invention, the efficiency of determining the beam weight is improved, so that the communication efficiency of the user terminal is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a beam weight determination method, a base station, and a storage medium. Background Art

[0002] In the field of communications, a directional beam is formed by BF (Beam Forming), so that the signal forms energy convergence in space and accurately points to the direction of the user terminal, which can improve the signal-to-noise ratio of the signal received by the user terminal and improve the effective utilization rate of communication resources. At present, the base station generally scans the beams in the beam codebook one by one, and the user terminal calculates the RSRP (Reference Signal Receiving Power) of each beam, and feeds back the sequence number and RSRP of the K beams with the highest RSRP to the base station. The base station calculates the beam weight of the strongest path and determines to align with the strongest beam. This process consumes a lot of computing resources and is not efficient.

[0003] Therefore, how to improve the efficiency of determining beam weights to enhance the communication efficiency of user terminals has become an urgent problem to be solved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a beam weight determination method, a base station and a storage medium, aiming to improve the efficiency of determining the beam weight so as to improve the communication efficiency of the user terminal.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a beam weight, the method comprising:

[0006] Positioning a user terminal accessing a base station cell to obtain a current coordinate of the user terminal;

[0007] According to the current coordinates, a preset fingerprint library is queried to determine the beam weight currently corresponding to the user terminal, wherein the base station cell coverage area is divided into multiple target areas, and the fingerprint library includes a mapping relationship between the reference coordinates of each target area and the beam weight.

[0008] In a second aspect, an embodiment of the present application provides a base station, the base station comprising:

[0009] A processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of the beam weight determination method as described above are realized.

[0010] In a third aspect, an embodiment of the present application provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the beam weight determination method as described above.

[0011] The embodiment of the present application provides a beam weight determination method, a base station and a storage medium. The method divides the coverage area of ​​a base station cell into multiple target areas and presets a fingerprint library, wherein the fingerprint library includes a mapping relationship between the reference coordinates of each target area and the beam weight. When a user terminal accesses the base station cell, the user terminal is positioned to obtain the current coordinates of the user terminal. According to the current coordinates of the user terminal, the fingerprint library is queried to determine the beam weight currently corresponding to the user terminal. This reduces the processing flow of the base station calculating the beam weight according to the information fed back by the user terminal, reduces the consumption of computing resources, and therefore improves the efficiency of determining the beam weight, thereby improving the communication efficiency of the user terminal and reducing the communication delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a beam weight determination method provided by an embodiment of the present invention;

[0014] Figure 2 A schematic diagram of a wireless communication system provided by an embodiment of the present invention;

[0015] Figure 3 A schematic diagram of a process for generating a fingerprint library provided by an embodiment of the present invention;

[0016] Figure 4 A schematic flow chart of a step of querying a preset fingerprint library according to the current coordinates to determine the beam weight currently corresponding to the user terminal provided by an embodiment of the present invention;

[0017] Figure 5 A schematic diagram of a process for updating a fingerprint database provided by an embodiment of the present invention;

[0018] Figure 6 A schematic diagram of a fingerprint library optimization process provided by an embodiment of the present invention;

[0019] Figure 7 A flowchart of user terminal beam management provided by an embodiment of the present invention;

[0020] Figure 8 A schematic block diagram of the structure of a base station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0023] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] Currently, the base station generally scans the beams in the beam codebook one by one, and the user terminal calculates the RSRP of each beam and feeds back the sequence numbers and RSRP of the K beams with the highest RSRP to the base station. The base station calculates the beam weight of the strongest path and determines to align with the strongest beam. This process consumes a lot of computing resources and is inefficient.

[0025] In order to solve the above problems, the embodiments of the present invention provide a beam weight determination method, a base station and a storage medium, aiming to improve the efficiency of determining beam weights so as to improve the communication efficiency of user terminals.

[0026] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] Please refer to Figure 1 , Figure 1 A schematic flow chart of a beam weight determination method provided in an embodiment of the present invention.

[0028] like Figure 1 As shown, the beam weight determination method includes step S101 and step S102.

[0029] S101: Position a user terminal accessing a base station cell to obtain current coordinates of the user terminal.

[0030] Exemplarily, the base station includes but is not limited to NR (New Radio) base station, and the user terminal includes but is not limited to smart phones, smart watches, etc.

[0031] For example, Figure 2 As shown, in a wireless communication system, including a user terminal and a base station, when the user terminal accesses the wireless network and successfully accesses the base station cell, the base station locates the user terminal and obtains the current coordinates of the user terminal. For example, the user terminal is located through high-precision positioning technologies such as UTDOA (Uplink Time Difference of Arrival) and AOA (Angle of Arrival) to obtain the current coordinates of the user terminal.

[0032] It should be noted that the present application does not impose any specific restrictions on the method of positioning the user terminal.

[0033] S102. According to the current coordinates, query a preset fingerprint library to determine the beam weight currently corresponding to the user terminal, wherein the base station cell coverage area is divided into multiple target areas, and the fingerprint library includes a mapping relationship between the reference coordinates of each of the target areas and the beam weight.

[0034] In the present application, the base station cell coverage area is pre-divided into a plurality of target areas, wherein the target areas include but are not limited to grid areas or other types of areas.

[0035] For example, the base station cell coverage area can be divided into multiple target areas, such as multiple grid areas, by manual on-site dot measurement. Alternatively, the base station cell coverage area can be divided into multiple target areas, such as multiple grid areas, according to set sizes by video machine vision scanning.

[0036] Based on the divided target areas, a fingerprint library corresponding to the beam weights is generated, wherein the fingerprint library includes a mapping relationship between the reference coordinates of each target area and the beam weights, and the reference coordinates of the target area include but are not limited to the center coordinates of the target area.

[0037] In some embodiments, Figure 3 As shown, step S103 to step S105 may be included before step S101.

[0038] S103, obtaining the center coordinates of each target area divided by the base station cell coverage area, and using the center coordinates as reference coordinates of the target area;

[0039] S104, initiating user terminal access at a position corresponding to the reference coordinates of each target area, and acquiring a beam weight corresponding to the user terminal;

[0040] S105: Determine a mapping relationship between the reference coordinates of each target area and the corresponding beam weight, and generate the fingerprint library.

[0041] Taking the target area as a grid area as an example, the base station cell coverage area is divided into a plurality of grid areas, wherein the size of each grid area can be flexibly set according to actual conditions.

[0042] Afterwards, the center coordinates of each grid area divided by the base station cell are obtained, and the center coordinates are used as reference coordinates of the grid area. Exemplarily, the center coordinates (x, y, z) of each grid area are determined, and the center coordinates (x, y, z) are used as reference coordinates of the grid area.

[0043] User terminal access is initiated at the position corresponding to the reference coordinate of each grid area, and the user terminal traverses and accesses the positions corresponding to the reference coordinates of each grid area. For example, user terminal access is initiated at the center coordinate (x, y, z) position of each grid area. After the user terminal accesses, the base station performs beam scanning, and the user terminal calculates the RSRP (Reference Signal Receiving Power) of each beam, and reports the optimal beam information to the base station, wherein the optimal beam information includes multiple beam numbers and RSRPs with the highest RSRPs.

[0044] The base station obtains the beam weight of the corresponding channel based on the optimal beam information reported by the user terminal, and generates a beam aimed at the strongest path. The beam aimed at the strongest path has the greatest signal strength. For example, the beam weight corresponding to downlink channels such as PDSCH (Physical Downlink Shared Channel) and PDCCH (Physical Downlink Control Channel) is obtained.

[0045] According to the reference coordinates of each grid area and the beam weight corresponding to the reference coordinate position of each grid area, a mapping relationship between the reference coordinates of each grid area and the corresponding beam weight is established to generate a fingerprint library. For example, a mapping relationship between the center coordinates (x, y, z) of each grid area and the corresponding beam weight is established to generate a fingerprint library.

[0046] Exemplarily, the generated fingerprint library is stored in the base station unit. It is understandable that the fingerprint library can also be stored in other storage units.

[0047] When the user terminal successfully accesses the base station cell, the base station locates the user terminal and obtains the current coordinates of the user terminal. Then, the base station queries the fingerprint library based on the current coordinates of the user terminal, matches the current coordinates of the user terminal with the reference coordinates of each target area in the fingerprint library, determines the beam weight corresponding to the reference coordinates of the matched target area, and determines the beam weight as the beam weight currently corresponding to the user terminal. Then, based on the determined beam weight, a beam aligned with the strongest path is obtained for use by the user terminal.

[0048] In some embodiments, Figure 4 As shown, step S102 may include sub-step S1021 and sub-step S1022.

[0049] S1021, querying the fingerprint database to determine a target reference coordinate that matches the current coordinate, the target reference coordinate being a reference coordinate of a target area having the highest feature similarity to the current coordinate;

[0050] S1022. According to a mapping relationship between the reference coordinates of the target area and the beam weight, determine the beam weight mapped by the target reference coordinates as the beam weight currently corresponding to the user terminal.

[0051] After the base station locates the user terminal and obtains the current coordinates of the user terminal, it queries the fingerprint library based on the current coordinates of the user terminal, determines the reference coordinates of the target area with the highest similarity to the current coordinate features of the user terminal, and determines the reference coordinates of the target area with the highest similarity to the current coordinate features as the reference coordinates matched with the current coordinates. For the convenience of distinguishing descriptions, the reference coordinates of the target area with the highest similarity to the current coordinate features that are matched are referred to as target reference coordinates below.

[0052] Exemplarily, the current coordinates of the user terminal are compared with the reference coordinates of each target area in the fingerprint library, and the reference coordinates of the target area closest to the current coordinates of the user terminal are determined as the reference coordinates of the target area with the highest feature similarity to the current coordinates of the user terminal. In other words, the reference coordinates of the target area closest to the current coordinates of the user terminal are determined as the target reference coordinates that match the current coordinates of the user terminal.

[0053] Exemplarily, based on the current coordinates of the user terminal, multiple reference coordinates within a set distance range from the current coordinates of the user terminal are queried in the fingerprint library, and based on the mapping relationship between the multiple reference coordinates and the corresponding beam weights, the reference coordinates corresponding to the beam with the strongest RSRP are determined, and the reference coordinates corresponding to the beam with the strongest RSRP are determined as the reference coordinates of the target area with the highest similarity to the current coordinate features of the user terminal, that is, determined as the target reference coordinates that match the current coordinates of the user terminal.

[0054] The target reference coordinates are determined, and according to the mapping relationship between the reference coordinates of the target area in the fingerprint library and the beam weights, the beam weights mapped by the target reference coordinates are determined as the beam weights currently corresponding to the user terminal, thereby generating a beam aligned with the strongest path. That is, the base station can quickly determine the beam weights currently corresponding to the user terminal by simply querying the fingerprint library without the need for beam scanning. The user terminal reports the optimal beam information to the base station, and the base station obtains the beam weights of the corresponding channel based on the optimal beam information reported by the user terminal. This operation does not require a large amount of computing resources, thereby improving the efficiency of determining the beam weights.

[0055] In some embodiments, Figure 5 As shown, step S106 and step S107 may be included after step S105.

[0056] S106. Obtain a measurement result of a signal quality measurement performed by the user terminal based on each beam, wherein the measurement result includes a signal strength;

[0057] S107. If the beam currently used by the user terminal is not the beam with the greatest signal strength, determine the beam weight of the channel corresponding to the beam with the greatest signal strength, and update the fingerprint library with the beam weight of the channel corresponding to the beam with the greatest signal strength.

[0058] Exemplarily, the signal strength in the measurement result of the signal quality measurement includes but is not limited to RSRP.

[0059] The base station performs beam scanning and measures the signal quality of each beam. The user terminal calculates the RSRP of each beam and reports it to the base station. The base station obtains the measurement results of the signal quality measurement of each beam based on the RSRP of each beam reported by the user terminal.

[0060] According to the measurement results, the base station determines the beam with the greatest signal strength. If the beam currently used by the user terminal happens to be the beam with the greatest signal strength, the user terminal is controlled to continue using the currently used beam. If the beam currently used by the user terminal is not the beam with the greatest signal strength, the base station obtains the beam weight of the corresponding channel based on the multiple beam numbers and RSRPs with the highest RSRP fed back by the user terminal after the beam scan, that is, the beam weight of the channel corresponding to the beam with the greatest signal strength is obtained. Based on the beam weight of the channel corresponding to the beam with the greatest signal strength, the fingerprint library is updated, thereby increasing and optimizing the fingerprint library data set. The updated fingerprint library is subsequently called to determine the beam weight currently corresponding to the user terminal, and the reliability of determining the beam aligned with the strongest path is higher.

[0061] Exemplarily, updating the fingerprint library with the beam weight of the channel corresponding to the beam with the maximum signal strength includes:

[0062] Determine a mapping relationship between a beam weight of a channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal, and update the fingerprint library.

[0063] After obtaining the current coordinates of the user terminal and determining the beam weight of the channel corresponding to the beam with the largest signal strength through signal quality measurement, a mapping relationship between the beam weight of the channel corresponding to the beam with the largest signal strength and the current coordinates of the user terminal is established, stored in the fingerprint library, and the fingerprint library is updated.

[0064] In some embodiments, if the beam currently used by the user terminal is not the beam with the greatest signal strength, determining the beam weight of the channel corresponding to the beam with the greatest signal strength includes:

[0065] If the currently used beam is not the beam with the largest signal strength, determining a signal strength difference between the currently used beam and the beam with the largest signal strength;

[0066] If the signal strength difference is greater than a preset threshold, the beam weight of the channel corresponding to the beam with the largest signal strength is determined, and the fingerprint library is updated with the beam weight of the channel corresponding to the beam with the largest signal strength.

[0067] In actual applications, it is very likely that the beam currently used by the user terminal is not the beam with the highest signal strength, but the difference in signal strength between the two is very small. At this time, it is not meaningful to update the fingerprint library. Therefore, in order to reduce the processing volume of fingerprint library updates, a preset threshold corresponding to the signal strength for determining whether to update the fingerprint library is pre-set. It should be noted that the preset threshold can be flexibly set according to the actual situation, and is not specifically limited in this application.

[0068] If the beam currently used by the user terminal is not the beam with the highest signal strength determined by the signal quality measurement, at this time, the signal strength difference between the beam currently used by the user terminal and the beam with the highest signal strength is calculated. The determined signal strength difference is compared with the preset threshold. If the signal strength difference is less than or equal to the preset threshold, there is no need to update the fingerprint library, and the user terminal continues to use the currently used beam. If the signal strength difference is greater than the preset threshold, the beam weight of the channel corresponding to the beam with the highest signal strength is determined, and a mapping relationship between the beam weight of the channel corresponding to the beam with the highest signal strength and the current coordinates of the user terminal is established and stored in the fingerprint library, and the fingerprint library is updated.

[0069] Exemplarily, the RSRP difference between the beam currently used by the user terminal and the beam with the largest signal strength is calculated, and the RSRP difference is compared with the preset RSRP threshold. If the RSRP difference is less than or equal to the RSRP threshold, there is no need to update the fingerprint library, and the user terminal continues to use the currently used beam. If the RSRP difference is greater than the RSRP threshold, the beam weight of the channel corresponding to the beam with the largest signal strength is determined, and a mapping relationship between the beam weight of the channel corresponding to the beam with the largest signal strength and the current coordinates of the user terminal is established, stored in the fingerprint library, and the fingerprint library is updated.

[0070] In some embodiments, Figure 6 As shown, step S108 may be included after step S107.

[0071] S108. Input the updated fingerprint library into the machine learning model, and perform fitting processing on at least one reference coordinate and beam weight of each target area to optimize the fingerprint library, wherein the updated fingerprint library includes a mapping relationship between at least one reference coordinate and beam weight of each target area.

[0072] After the fingerprint library is updated, for each target area, in addition to the mapping relationship between the center coordinates of the target area and the corresponding beam weights, the updated fingerprint library will also include the mapping relationship between the current coordinates of the updated user terminal and the corresponding beam weights, that is, each target area corresponds to one or more reference coordinates. With the continuous updating of the fingerprint library, the data sets of the mapping relationship between the reference coordinates and the beam weights corresponding to each target area will increase, and too many will cause redundancy. Therefore, in order to further optimize the fingerprint library, a machine learning algorithm, such as a clustering algorithm, is used to perform cluster fitting on the mapping relationship between the reference coordinates and the beam weights corresponding to each target area, so as to obtain the mapping relationship between the reference coordinates and the corresponding beam weights corresponding to each target area (such as a grid area) in the fingerprint library.

[0073] Exemplarily, a machine learning model is pre-trained based on sample data, and the updated fingerprint library is input into the trained machine learning model. The machine learning model is used to perform cluster fitting processing on multiple reference coordinates and beam weights corresponding to each target area in the fingerprint library, so as to obtain a mapping relationship between the reference coordinates and beam weights corresponding to each target area, thereby optimizing the fingerprint library.

[0074] In some embodiments, inputting the updated fingerprint library into a machine learning model and performing fitting processing on at least one reference coordinate of each target area and a beam weight includes:

[0075] According to a preset cycle time, regularly inputting the updated fingerprint library into the machine learning model to perform fitting processing on at least one reference coordinate and beam weight of each target area; or

[0076] When the average number of reference coordinates of each target area reaches a preset number, the updated fingerprint library is input into the machine learning model, and fitting processing is performed on at least one reference coordinate of each target area and the beam weight.

[0077] Exemplarily, the cycle time for optimizing the fingerprint library by the machine learning model is pre-set. It should be noted that the cycle time can be flexibly set according to actual conditions and is not specifically limited in this application.

[0078] According to the preset cycle time, the updated fingerprint library is regularly input into the machine learning model. The machine learning model is used to perform cluster fitting processing on multiple reference coordinates and beam weights corresponding to each target area in the fingerprint library to obtain the mapping relationship between the reference coordinates and beam weights corresponding to each target area, and optimize the fingerprint library.

[0079] Exemplarily, a preset number of reference coordinates corresponding to each target area for optimizing the fingerprint library by the machine learning model is preset. It should be noted that the preset number can be flexibly set according to actual conditions and is not specifically limited in this application.

[0080] The number of mapping relationships between reference coordinates and beam weights corresponding to each target area in the fingerprint library is counted, and based on the number of mapping relationships between reference coordinates and beam weights corresponding to each target area, the number mean of the number of mapping relationships between reference coordinates and beam weights corresponding to each target area is determined. The number mean is compared with the preset number. If the number mean does not reach the preset number, it is not currently necessary to perform cluster fitting processing on the multiple reference coordinates and beam weights corresponding to each target area in the fingerprint library through the machine learning model. If the number mean reaches the preset number, at this time, the multiple reference coordinates and beam weights corresponding to each target area in the fingerprint library are clustered and fitted by the machine learning model to obtain the mapping relationship between the reference coordinates and beam weights corresponding to each target area.

[0081] Below, as Figure 7 As shown, the process of user terminal beam management is as follows:

[0082] 1) Confirm the coverage area of ​​the base station cell, divide the coverage area into multiple grid areas, and record the center coordinates of each grid area.

[0083] 2) Initiate user terminal access at the center coordinate position of the grid area, and determine the beam weight of the corresponding beam downlink channel based on the optimal beam information reported by the user terminal.

[0084] 3) Establish a mapping relationship between the center coordinates of each grid area and the beam weight of the downlink channel, generate a fingerprint library, and store it in the base station unit.

[0085] 4) After a new user terminal is connected, the spatial coordinates (x, y, z) of the user terminal are obtained through high-precision positioning technology, such as UTDOA, AOA, etc., and the fingerprint library is queried. The center coordinates of the grid area and the beam weight are obtained according to the principle of the closest distance to the spatial coordinates (x, y, z), and the configuration is implemented. Exemplarily, the spatial coordinates (x, y, z) of the user terminal can be updated periodically.

[0086] 5) Compare the RSRP measurement results of the beams after the user terminal accesses in real time. If the RSRP difference between the optimal beam and the currently used beam exceeds the RSRP threshold (the RSRP threshold is configurable), calculate the beam weight of the downlink channel corresponding to the optimal beam and update the fingerprint library.

[0087] 6) In the statistically updated fingerprint library, each grid area includes a plurality of mapping relationships between spatial coordinates and beam weights. A machine learning algorithm (such as a clustering algorithm) is periodically (the period is configurable) used for clustering fitting to generate a new mapping relationship between the spatial coordinates in each grid area and the beam weight of the downlink channel, thereby optimizing the fingerprint library.

[0088] By using high-precision positioning technology, the mapping relationship between the spatial coordinates in the grid area and the beam weights of the downlink channel is established, a fingerprint library is generated, and combined with a machine learning algorithm, cluster fitting is performed on the mapping relationship between each spatial coordinate in the grid area and the beam weights. The determination of the beam weights not only reduces the base station computing resources, but also reduces the downlink processing delay, thereby improving efficiency.

[0089] In the above embodiment, the coverage area of ​​the base station cell is divided into multiple target areas, and a fingerprint library is preset, which includes the mapping relationship between the reference coordinates of each target area and the beam weight. When the user terminal accesses the base station cell, the user terminal is positioned to obtain the current coordinates of the user terminal. According to the current coordinates of the user terminal, the fingerprint library is queried to determine the beam weight currently corresponding to the user terminal, thereby eliminating the need for the base station to calculate the beam weight based on the information fed back by the user terminal, reducing the consumption of computing resources, and therefore improving the efficiency of determining the beam weight, thereby improving the communication efficiency of the user terminal.

[0090] The present invention also provides a base station. Figure 8 , Figure 8 It is a schematic block diagram of a base station provided in one embodiment of the present application.

[0091] like Figure 8As shown, the base station 200 may include a processor 210 and a memory 220, wherein the processor 210 and the memory 220 are connected via a bus, such as an I2C (Inter-integrated Circuit) bus.

[0092] Specifically, the processor 210 may be a micro-controller unit (MCU), a central processing unit (CPU), or a digital signal processor (DSP).

[0093] Specifically, the memory 220 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB disk, or a mobile hard disk, etc. The memory 220 stores various computer programs for the processor 210 to execute.

[0094] The processor 210 is used to run a computer program stored in the memory, and implements the following steps when executing the computer program:

[0095] Positioning a user terminal accessing a base station cell to obtain a current coordinate of the user terminal;

[0096] According to the current coordinates, a preset fingerprint library is queried to determine the beam weight currently corresponding to the user terminal, wherein the base station cell coverage area is divided into multiple target areas, and the fingerprint library includes a mapping relationship between the reference coordinates of each target area and the beam weight.

[0097] In some embodiments, when the processor 210 implements the querying of a preset fingerprint library according to the current coordinates to determine the beam weight currently corresponding to the user terminal, it is configured to implement:

[0098] Query the fingerprint library to determine the target reference coordinates matching the current coordinates, the target reference coordinates being the reference coordinates of the target area with the highest feature similarity to the current coordinates;

[0099] According to the mapping relationship between the reference coordinates of the target area and the beam weight, the beam weight mapped by the target reference coordinates is determined as the beam weight currently corresponding to the user terminal.

[0100] In some embodiments, before implementing the positioning of the user terminal accessing the base station cell and obtaining the current coordinates of the user terminal, the processor 210 is used to implement:

[0101] Obtaining the center coordinates of each of the target areas divided into the base station cell coverage area, and using the center coordinates as reference coordinates of the target area;

[0102] Initiating user terminal access at a position corresponding to the reference coordinates of each target area to obtain a beam weight corresponding to the user terminal;

[0103] A mapping relationship between the reference coordinates of each target area and the corresponding beam weight is determined to generate the fingerprint library.

[0104] In some embodiments, the processor 210 is further configured to implement:

[0105] Obtaining a measurement result of a signal quality measurement performed by the user terminal based on each beam, wherein the measurement result includes a signal strength;

[0106] If the beam currently used by the user terminal is not the beam with the greatest signal strength, the beam weight of the channel corresponding to the beam with the greatest signal strength is determined, and the fingerprint library is updated with the beam weight of the channel corresponding to the beam with the greatest signal strength.

[0107] In some embodiments, when the processor 210 implements the step of determining the beam weight of the channel corresponding to the beam with the greatest signal strength if the beam currently used by the user terminal is not the beam with the greatest signal strength, it is configured to implement:

[0108] If the currently used beam is not the beam with the largest signal strength, determining a signal strength difference between the currently used beam and the beam with the largest signal strength;

[0109] If the signal strength difference is greater than a preset threshold, the beam weight of the channel corresponding to the beam with the largest signal strength is determined, and the fingerprint library is updated with the beam weight of the channel corresponding to the beam with the largest signal strength.

[0110] In some embodiments, when the processor 210 implements updating the fingerprint library with the beam weight of the channel corresponding to the beam with the maximum signal strength, it is configured to implement:

[0111] Determine a mapping relationship between a beam weight of a channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal, and update the fingerprint library.

[0112] In some embodiments, the processor 210 is further configured to implement:

[0113] The updated fingerprint library is input into the machine learning model, and at least one reference coordinate and beam weight of each target area are fitted to optimize the fingerprint library, wherein the updated fingerprint library includes a mapping relationship between at least one reference coordinate and beam weight of each target area.

[0114] In some embodiments, when the processor 210 implements the inputting of the updated fingerprint library into the machine learning model and performing fitting processing on at least one reference coordinate of each target area and the beam weight, it is used to implement:

[0115] According to a preset cycle time, regularly inputting the updated fingerprint library into the machine learning model to perform fitting processing on at least one reference coordinate and beam weight of each target area; or

[0116] When the average number of reference coordinates of each target area reaches a preset number, the updated fingerprint library is input into the machine learning model, and fitting processing is performed on at least one reference coordinate of each target area and the beam weight.

[0117] An embodiment of the present application also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any beam weight determination method provided in the description of the embodiment of the present application.

[0118] The storage medium may be an internal storage unit of the base station described in the foregoing embodiment, such as a hard disk or memory of the base station. The storage medium may also be an external storage device of the base station, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the base station.

[0119] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0120] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0121] The serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for determining a beam weight, the method comprising: Positioning a user terminal accessing a base station cell to obtain a current coordinate of the user terminal; According to the current coordinates, a preset fingerprint library is queried to determine the beam weight currently corresponding to the user terminal, wherein the base station cell coverage area is divided into multiple target areas, and the fingerprint library includes a mapping relationship between the reference coordinates of each target area and the beam weight.

2. The method according to claim 1, characterized in that The querying a preset fingerprint library according to the current coordinates to determine the beam weight currently corresponding to the user terminal includes: Query the fingerprint library to determine the target reference coordinates matching the current coordinates, the target reference coordinates being the reference coordinates of the target area with the highest feature similarity to the current coordinates; According to the mapping relationship between the reference coordinates of the target area and the beam weight, the beam weight mapped by the target reference coordinates is determined as the beam weight currently corresponding to the user terminal.

3. The method according to claim 1, characterized in that Before positioning the user terminal accessing the base station cell and obtaining the current coordinates of the user terminal, the method includes: Obtaining the center coordinates of each of the target areas divided into the base station cell coverage area, and using the center coordinates as reference coordinates of the target area; Initiating user terminal access at a position corresponding to the reference coordinates of each target area to obtain a beam weight corresponding to the user terminal; A mapping relationship between the reference coordinates of each target area and the corresponding beam weight is determined to generate the fingerprint library.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Obtaining a measurement result of a signal quality measurement performed by the user terminal based on each beam, wherein the measurement result includes a signal strength; If the beam currently used by the user terminal is not the beam with the greatest signal strength, the beam weight of the channel corresponding to the beam with the greatest signal strength is determined, and the fingerprint library is updated with the beam weight of the channel corresponding to the beam with the greatest signal strength.

5. The method according to claim 4, characterized in that If the beam currently used by the user terminal is not the beam with the greatest signal strength, determining the beam weight of the channel corresponding to the beam with the greatest signal strength includes: If the currently used beam is not the beam with the largest signal strength, determining a signal strength difference between the currently used beam and the beam with the largest signal strength; If the signal strength difference is greater than a preset threshold, the beam weight of the channel corresponding to the beam with the largest signal strength is determined, and the fingerprint library is updated with the beam weight of the channel corresponding to the beam with the largest signal strength.

6. The method according to claim 4, characterized in that The updating of the fingerprint library with the beam weight of the channel corresponding to the beam with the maximum signal strength includes: Determine a mapping relationship between a beam weight of a channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal, and update the fingerprint library.

7. The method according to claim 4, characterized in that The method further comprises: The updated fingerprint library is input into the machine learning model, and at least one reference coordinate and beam weight of each target area are fitted to optimize the fingerprint library, wherein the updated fingerprint library includes a mapping relationship between at least one reference coordinate and beam weight of each target area.

8. The method according to claim 7, characterized in that The step of inputting the updated fingerprint library into a machine learning model and performing cluster fitting processing on at least one reference coordinate and beam weight of each target area includes: According to a preset cycle time, regularly inputting the updated fingerprint library into the machine learning model to perform fitting processing on at least one reference coordinate and beam weight of each target area; or When the average number of reference coordinates of each target area reaches a preset number, the updated fingerprint library is input into the machine learning model, and fitting processing is performed on at least one reference coordinate of each target area and the beam weight.

9. A base station, characterized in that: The base station comprises: A processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of the beam weight determination method according to any one of claims 1 to 8 are realized.

10. A storage medium for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the beam weight determination method as described in any one of claims 1 to 8.

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