Beam weight determination method, base station, and storage medium

Abstract

Embodiments of the present disclosure relate to the technical field of communications, and provide a beam weight determination method, a base station, and a storage medium. The beam weight determination method comprises: locating a user terminal accessing a base station cell to obtain current coordinates of the user terminal; and querying a preset fingerprint repository on the basis of the current coordinates, and determining a beam weight currently corresponding to the user terminal, wherein a coverage area of the base station cell is divided into a plurality of target areas, and the fingerprint repository comprises a mapping relationship between the reference coordinates of each target area and the beam weight.

Description

Beam weight determination method, base station and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on and claims the priority of Chinese patent application 202310761896.4 filed on June 26, 2023, and all the disclosed contents thereof are incorporated into this disclosure by reference. Technical Field

[0003] The present disclosure 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

[0004] In the communications field, beam forming (BF) forms a directional beam, converging signal energy in space and precisely pointing it in the direction of the user terminal. This improves the signal-to-noise ratio (SNR) of the received signal at the user terminal and increases the efficient use of communication resources. Currently, the base station typically scans the beams in the beam codebook one by one. The user terminal calculates the RSRP (Reference Signal Received Power) of each beam and feeds the sequence numbers and RSRPs of the K beams with the highest RSRPs back to the base station. The base station then calculates the beam weight of the strongest path and determines the strongest beam to align with. This process consumes a large amount of computing resources and is inefficient.

[0005] 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.

[0006] Summary of the Invention

[0007] The main purpose of the embodiments of the present disclosure is to provide a beam weight determination method, a base station, and a storage medium, aiming to improve the efficiency of determining beam weights to enhance the communication efficiency of user terminals.

[0008] In a first aspect, an embodiment of the present disclosure provides a method for determining a beam weight, the method comprising:

[0009] Positioning the user terminal connected to the base station cell to obtain the current coordinates of the user terminal;

[0010] According to the current coordinates, the preset fingerprint library is queried to determine the beam weight currently corresponding to the user terminal. The base station cell coverage area is divided into multiple target areas, and the fingerprint library includes the mapping relationship between the reference coordinates of each target area and the beam weight.

[0011] In a second aspect, an embodiment of the present disclosure provides a base station, the base station including:

[0012] 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.

[0013] In a third aspect, an embodiment of the present disclosure 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0015] FIG1 is a schematic flow chart of a beam weight determination method provided by an embodiment of the present disclosure;

[0016] FIG2 is a schematic diagram of a wireless communication system provided by an embodiment of the present disclosure;

[0017] FIG3 is a schematic diagram of a process for generating a fingerprint library according to an embodiment of the present disclosure;

[0018] FIG4 is a flowchart illustrating a step of querying a preset fingerprint library to determine the beam weight currently corresponding to the user terminal according to the current coordinates, provided by an embodiment of the present disclosure;

[0019] FIG5 is a schematic diagram of a process for updating a fingerprint database according to an embodiment of the present disclosure;

[0020] FIG6 is a schematic diagram of a fingerprint library optimization process according to an embodiment of the present disclosure;

[0021] FIG7 is a flowchart of a user terminal beam management according to an embodiment of the present disclosure;

[0022] FIG8 is a schematic block diagram of the structure of a base station provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0024] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all 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 vary depending on the actual situation.

[0025] It should be understood that the terms used in this disclosure are only for the purpose of describing particular embodiments and are not intended to limit the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] Currently, the base station typically scans the beams in the beam codebook one by one. The user terminal calculates the RSRP of each beam and feeds back the sequence numbers and RSRPs of the K beams with the highest RSRPs to the base station. The base station then calculates the beam weight of the strongest path and determines the strongest beam to align with. This process consumes a large amount of computing resources and is inefficient.

[0027] In order to solve the above problems, the embodiments of the present disclosure provide a beam weight determination method, a base station, and a storage medium, aiming to improve the efficiency of determining beam weights to enhance the communication efficiency of user terminals.

[0028] The following embodiments of the present disclosure are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0029] Please refer to FIG. 1 , which is a flow chart of a beam weight determination method provided by an embodiment of the present disclosure.

[0030] As shown in FIG1 , the beam weight determination method includes step S101 and step S102 .

[0031] S101: Position a user terminal accessing a base station cell to obtain current coordinates of the user terminal.

[0032] Exemplarily, the base station includes but is not limited to NR (New Radio) base stations, and the user terminal includes but is not limited to smartphones, smart watches, etc.

[0033] For example, as shown in Figure 2, in a wireless communication system including a user terminal and a base station, when a user terminal accesses the wireless network and successfully connects to the base station cell, the base station locates the user terminal and obtains the user terminal's current coordinates. For example, high-precision positioning technologies such as UTDOA (Uplink Time Difference of Arrival) and AOA (Angle of Arrival) are used to locate the user terminal and obtain the user terminal's current coordinates.

[0034] It should be noted that the present disclosure does not impose any specific restrictions on the method of positioning the user terminal.

[0035] S102. Query a preset fingerprint library based on the current coordinates 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.

[0036] In the present disclosure, the base station cell coverage area is pre-divided into multiple target areas, wherein the target areas include but are not limited to grid areas or other types of areas.

[0037] For example, the base station cell coverage area can be divided into multiple target areas, such as multiple grid areas, by manual on-site point measurement. Alternatively, the base station cell coverage area can be divided into multiple target areas, such as multiple grid areas, according to a set size by video machine vision scanning.

[0038] 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.

[0039] In some embodiments, as shown in FIG3 , step S103 to step S105 may be included before step S101 .

[0040] S103: Obtain the center coordinates of each target area divided into the base station cell coverage area, and use the center coordinates as reference coordinates of the target area;

[0041] S104: Initiate user terminal access at a position corresponding to the reference coordinates of each target area, and obtain a beam weight corresponding to the user terminal;

[0042] S105: Determine a mapping relationship between the reference coordinates of each target area and the corresponding beam weight, and generate the fingerprint library.

[0043] Taking the target area as a grid area as an example, the base station cell coverage area is divided into multiple grid areas, wherein the size of each grid area can be flexibly set according to actual conditions.

[0044] Afterwards, the center coordinates of each grid area divided by the base station cell are obtained and used as the reference coordinates of the grid area. Exemplarily, the center coordinates (x, y, z) of each grid area are determined and used as the reference coordinates of the grid area.

[0045] User terminal access is initiated at the reference coordinate corresponding position of each grid area, and the user terminal traverses and accesses the reference coordinate corresponding positions 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, where the optimal beam information includes multiple beam numbers and RSRPs with the highest RSRPs.

[0046] Based on the optimal beam information reported by the user terminal, the base station obtains the beam weight for the corresponding channel and generates a beam aligned with the strongest path. The beam aligned with the strongest path has the highest signal strength. For example, the base station obtains the beam weight for downlink channels such as the PDSCH (Physical Downlink Shared Channel) and the PDCCH (Physical Downlink Control Channel).

[0047] Based on the reference coordinates of each grid region and the beam weights corresponding to the reference coordinate positions of each grid region, a mapping relationship between the reference coordinates of each grid region and the corresponding beam weights is established to generate a fingerprint library. For example, a mapping relationship between the center coordinates (x, y, z) of each grid region and the corresponding beam weights is established to generate a fingerprint library.

[0048] 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.

[0049] When a user terminal successfully accesses a base station cell, the base station locates the user terminal and obtains the current coordinates of the user terminal. The base station then 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 current beam weight corresponding to the user terminal. Furthermore, based on the determined beam weight, a beam aligned with the strongest path is obtained for use by the user terminal.

[0050] In some embodiments, as shown in FIG. 4 , step S102 may include sub-step S1021 and sub-step S1022 .

[0051] S1021, querying the fingerprint database to determine a target reference coordinate that matches the current coordinate, where the target reference coordinate is the reference coordinate of the target area having the highest feature similarity with the current coordinate;

[0052] S1022: According to the mapping relationship between the reference coordinates of the target area and the beam weight, determine the beam weight mapped to the target reference coordinates as the beam weight currently corresponding to the user terminal.

[0053] After the base station locates the user terminal and obtains the current coordinates of the user terminal, it queries the fingerprint database based on the current coordinates of the user terminal and determines the reference coordinates of the target area with the most similarity to the current coordinates of the user terminal. The reference coordinates of the target area with the most similarity to the current coordinates are then determined as the reference coordinates that match the current coordinates. For ease of description, the reference coordinates of the target area with the most similarity to the current coordinates are referred to as the target reference coordinates.

[0054] 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 similarity to the current coordinate features of the user terminal. That is, 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.

[0055] 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.

[0056] After determining the target reference coordinates, the beam weights mapped to the target reference coordinates are used as the current beam weights for the user terminal based on the mapping relationship between the target area's reference coordinates and beam weights in the fingerprint library. This generates a beam aligned with the strongest path. In other words, the base station can quickly determine the current beam weights for the user terminal simply by 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 for the corresponding channel based on the optimal beam information reported by the user terminal. This operation does not consume a large amount of computing resources, thereby improving the efficiency of determining beam weights.

[0057] In some embodiments, as shown in FIG5 , step S105 may be followed by step S106 and step S107 .

[0058] 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;

[0059] 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 database with the beam weight of the channel corresponding to the beam with the greatest signal strength.

[0060] Illustratively, the signal strength in the measurement result of the signal quality measurement includes but is not limited to RSRP.

[0061] 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.

[0062] Based on the measurement results, the base station determines the beam with the strongest signal strength. If the beam currently used by the user terminal happens to be the beam with the strongest 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 strongest signal strength, the base station obtains the beam weight of the corresponding channel based on the multiple beam numbers and RSRPs with the highest RSRPs fed back by the user terminal after the beam scan, that is, the beam weight of the channel corresponding to the beam with the strongest signal strength is obtained. Based on the beam weight of the channel corresponding to the beam with the strongest 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, which increases the reliability of determining the beam aligned with the strongest path.

[0063] Exemplarily, updating the fingerprint library with the beam weight of the channel corresponding to the beam with the maximum signal strength includes:

[0064] Determine a mapping relationship between the beam weight of the channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal, and update the fingerprint library.

[0065] After obtaining the current coordinates of the user terminal and determining the beam weight of the channel corresponding to the beam with the maximum signal strength through signal quality measurement, a mapping relationship between the beam weight of the channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal is established, stored in the fingerprint library, and the fingerprint library is updated.

[0066] 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:

[0067] 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;

[0068] 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.

[0069] In actual applications, it's quite possible that the beam currently used by a user terminal isn't the one with the highest signal strength, but the difference in signal strength between the two beams is minimal. In this case, updating the fingerprint database is not very meaningful. Therefore, to reduce the processing load for fingerprint database updates, a preset threshold corresponding to the signal strength used to determine whether to update the fingerprint database is pre-set. It should be noted that this preset threshold can be flexibly set based on actual circumstances and is not specifically limited in this disclosure.

[0070] If the beam currently used by the user terminal is not the beam with the highest signal strength determined by the signal quality measurement, the signal strength difference between the beam currently used by the user terminal and the beam with the highest signal strength is calculated and determined. 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 is established between the beam weight of the channel corresponding to the beam with the highest signal strength and the current coordinates of the user terminal. The relationship is stored in the fingerprint library and the fingerprint library is updated.

[0071] Exemplarily, the RSRP difference between the beam currently used by the user terminal and the beam with the greatest signal strength is calculated, and the RSRP difference is compared with a preset RSRP threshold. If the RSRP difference is less than or equal to the RSRP threshold, the fingerprint library does not need to be updated, 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 greatest signal strength is determined, and a mapping relationship is established between the beam weight of the channel corresponding to the beam with the greatest signal strength and the current coordinates of the user terminal. This mapping relationship is stored in the fingerprint library, and the fingerprint library is updated.

[0072] In some embodiments, as shown in FIG6 , step S108 may be further included after step S107 .

[0073] S108. Input the updated fingerprint library into a machine learning model, and perform fitting processing on at least one reference coordinate of each target area and the beam weight to optimize the fingerprint library, wherein the updated fingerprint library includes a mapping relationship between at least one reference coordinate of each target area and the beam weight.

[0074] 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 contain the updated mapping relationship between the current coordinates of the user terminal and the corresponding beam weights, that is, each target area corresponds to one or more reference coordinates. As the fingerprint library is continuously updated, the data sets of the mapping relationship between the reference coordinates and beam weights corresponding to each target area will become more and more, 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 beam weights corresponding to each target area, thereby obtaining 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.

[0075] 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, thereby obtaining a mapping relationship between the reference coordinates and beam weights corresponding to each target area, and optimizing the fingerprint library.

[0076] 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:

[0077] According to a preset period, the updated fingerprint library is regularly input into the machine learning model to perform fitting processing on at least one reference coordinate and beam weight of each target area; or

[0078] 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.

[0079] Exemplarily, the cycle time for the machine learning model to optimize the fingerprint library 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 disclosure.

[0080] 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.

[0081] Exemplarily, a preset number of reference coordinates corresponding to each target area for optimizing the fingerprint library by the machine learning model is pre-set. It should be noted that the preset number can be flexibly set according to actual conditions and is not specifically limited in this disclosure.

[0082] The number of mapping relationships between the 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 the reference coordinates and beam weights corresponding to each target area, the number mean of the number of mapping relationships between the 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, there is currently no need 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.

[0083] As shown in FIG7 , the process of beam management for a user terminal is as follows:

[0084] 1) Confirm the base station cell coverage area, divide the coverage area into multiple grid areas, and record the center coordinates of each grid area.

[0085] 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.

[0086] 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.

[0087] 4) After a new user terminal is connected, the user terminal's spatial coordinates (x, y, z) are obtained through high-precision positioning technologies such as UTDOA and AOA. The fingerprint database is queried, and the grid area center coordinates and beam weights are obtained based on the principle of closest distance to the spatial coordinates (x, y, z). These coordinates are then configured and implemented. For example, the user terminal's spatial coordinates (x, y, z) can be updated periodically.

[0088] 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.

[0089] 6) In the statistically updated fingerprint library, each grid area includes multiple mapping relationships between spatial coordinates and beam weights. Periodically (the period is configurable) a machine learning algorithm (such as a clustering algorithm) is used for cluster 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.

[0090] By leveraging high-precision positioning technology, a mapping relationship between spatial coordinates within a grid area and the beam weights of the downlink channel is established, generating a fingerprint library. Combined with a machine learning algorithm, cluster fitting is performed on the mapping relationship between each spatial coordinate within the grid area and the beam weights. Determining beam weights not only reduces base station computing resources but also reduces downlink processing latency, thereby improving efficiency.

[0091] In the above embodiment, the base station cell coverage area is divided into multiple target areas, and a fingerprint library is preset, which includes a 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. This eliminates the need for the base station to calculate the beam weight based on the information fed back by the user terminal, reduces the consumption of computing resources, and thereby improves the efficiency of determining the beam weight, thereby improving the communication efficiency of the user terminal.

[0092] An embodiment of the present disclosure further provides a base station. Please refer to FIG8 , which is a schematic block diagram of a base station provided in an embodiment of the present disclosure.

[0093] As shown in FIG8 , 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.

[0094] Specifically, the processor 210 may be a micro-controller unit (MCU), a central processing unit (CPU), or a digital signal processor (DSP).

[0095] Specifically, the memory 220 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk, etc. The memory 220 stores various computer programs for execution by the processor 210 .

[0096] The processor 210 is configured to run a computer program stored in the memory and implement the following steps when executing the computer program:

[0097] Positioning a user terminal connected to a base station cell to obtain the current coordinates of the user terminal;

[0098] 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.

[0099] 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:

[0100] Querying the fingerprint database to determine a target reference coordinate that matches the current coordinate, the target reference coordinate being the reference coordinate of the target area having the highest feature similarity to the current coordinate;

[0101] According to the mapping relationship between the reference coordinates of the target area and the beam weight, the beam weight mapped to the target reference coordinate is determined as the beam weight currently corresponding to the user terminal.

[0102] In some embodiments, before implementing positioning of a user terminal accessing a base station cell and obtaining the current coordinates of the user terminal, the processor 210 is configured to implement:

[0103] 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;

[0104] Initiating user terminal access at a position corresponding to the reference coordinates of each target area, and obtaining a beam weight corresponding to the user terminal;

[0105] A mapping relationship between the reference coordinates of each target area and the corresponding beam weight is determined to generate the fingerprint library.

[0106] In some embodiments, the processor 210 is further configured to implement:

[0107] Obtaining a measurement result of a signal quality measurement performed by the user terminal based on each beam, the measurement result including a signal strength;

[0108] 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.

[0109] In some embodiments, when implementing, 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, the processor 210 is configured to implement:

[0110] 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;

[0111] 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.

[0112] In some embodiments, when implementing the updating of the fingerprint library using the beam weight of the channel corresponding to the beam with the maximum signal strength, the processor 210 is configured to implement:

[0113] Determine a mapping relationship between the beam weight of the channel corresponding to the beam with the maximum signal strength and the current coordinates of the user terminal, and update the fingerprint library.

[0114] In some embodiments, the processor 210 is further configured to implement:

[0115] The updated fingerprint library is input into the machine learning model, and at least one reference coordinate of each target area is fitted with the beam weight to optimize the fingerprint library, wherein the updated fingerprint library includes a mapping relationship between at least one reference coordinate of each target area and the beam weight.

[0116] In some embodiments, when inputting 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, the processor 210 is configured to implement:

[0117] According to a preset period, the updated fingerprint library is regularly input into the machine learning model to perform fitting processing on at least one reference coordinate and beam weight of each target area; or

[0118] 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.

[0119] The embodiments of the present disclosure also provide 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 embodiments of the present disclosure.

[0120] The storage medium may be the internal storage unit of the base station described in the preceding embodiments, 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 media card (SMC), a secure digital (SD) card, or a flash memory card equipped on the base station.

[0121] 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 is well 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 tape, 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 skilled in the art that communication media generally embodies 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.

[0122] It should be understood that the term "and / or" as used in this disclosure 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 variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are 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 that includes the element.

[0123] The serial numbers of the embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments. The above description is only a specific embodiment of the present disclosure, but the scope of protection of the present disclosure 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 disclosure, and these modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: The method further comprises: The updated fingerprint library is input into the machine learning model, and at least one reference coordinate of each target area is fitted with the beam weight to optimize the fingerprint library, wherein the updated fingerprint library includes each A mapping relationship between at least one reference coordinate of the target area and the beam weight.

8. The method according to claim 7, wherein: 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, comprising: 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, 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 in any one of claims 1 to 8.

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