Method for reconstructing wave beam characteristic parameters, method for determining wave beam, method for reconstructing object characteristic parameters and equipment

Abstract

The invention provides a method for reconstructing a beam characteristic parameter, a method for determining a beam, a method for reconstructing an object characteristic parameter and equipment, and relates to the technical field of data reconstruction, and the method for reconstructing the beam characteristic parameter applied to a user terminal comprises the following steps: receiving a reference signal of each beam in a beam subset sent by network equipment, the beam subsets comprise singular beams, and the correlation between the singular beams and other beams in a beam complete set where the beam subsets are located is lower than a correlation threshold value; measuring a measurement beam feature parameter of each beam in the beam subset based on the reference signal; and reconstructing predicted beam feature parameters of other beams except the beam subset in the beam complete set based on the measured beam feature parameters of each beam in the beam subset. According to the method, the wave beam characteristic parameters of the wave beams with high correlation can be obtained through reconstruction, the wave beam characteristic parameters of the wave beams with low correlation can also be obtained through measurement, and the accuracy of the prediction result of the wave beam universal set obtained through reconstruction is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of data reconstruction, and in particular to a method for reconstructing beam characteristic parameters, a method for determining a beam, a method for reconstructing object characteristic parameters, and an electronic device. Background Art

[0002] Reconstruction is a task or technique whose goal is to use a model to restore the original data in its complete form, that is, to reconstruct an estimated version of the original or target data based on partial or distorted information; in other words, to let the model guess the missing part or restore the obscured data. However, in the process of reconstructing the missing or obscured data from partial information, some of the prediction results are inaccurate, resulting in low accuracy of the reconstructed prediction results.

[0003] Taking the beam reconstruction scenario as an example, the spatial domain downlink beam reconstruction can be performed on the full set of beams based on the measurement results of the beam subset, thereby reducing signaling overhead and prediction delay. However, when the prediction results of the full set of beams are reconstructed using the measurement results of the beam subset, the prediction results of some beams are inaccurate, resulting in low accuracy of the prediction results of the reconstructed full set of beams. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for reconstructing beam characteristic parameters, a method for determining a beam, a method for reconstructing object characteristic parameters, and an electronic device, so as to solve the technical problem in the prior art that when the prediction results of the entire beam set are reconstructed through the measurement results of a beam subset, the accuracy of the reconstructed prediction results of the entire beam set is low.

[0005] In a first aspect, an embodiment of the present application provides a method for reconstructing beam characteristic parameters, which is applied to a user terminal, and the method comprises: receiving a reference signal of each beam in a beam subset sent by a network device, wherein the beam subset includes a singular beam, and the correlation between the singular beam and other beams in the entire beam set where the beam subset is located is lower than a correlation threshold; measuring the measured beam characteristic parameters of each beam in the beam subset based on the reference signal; and reconstructing the predicted beam characteristic parameters of other beams in the entire beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0006] In the above scheme, in the process of reconstructing the predicted beam characteristic parameters of other beams in the beam set except the beam subset by using the measured beam characteristic parameters of each beam in the beam subset, a singular beam with low correlation with other beams in the beam set can be added to the beam subset, so that even if the beam characteristic parameters corresponding to the singular beam cannot be obtained by reconstruction, the corresponding beam characteristic parameters can be obtained by measurement. Therefore, the method provided in the embodiment of the present application can obtain the beam characteristic parameters of beams with high correlation by reconstruction, and can also obtain the beam characteristic parameters of beams with low correlation by measurement, thereby improving the accuracy of the prediction results of the reconstructed beam set.

[0007] In an optional embodiment, the method further includes: receiving reference signals of each beam in the beam set sent by the network device at different times; for any time, measuring the measured beam characteristic parameters of each beam in the beam set based on the reference signal sent at that time; determining the singular beam in the beam set according to the measured beam characteristic parameters corresponding to at least one time; and sending beam information corresponding to the singular beam to the network device. In the above scheme, since the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam set through the measured beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the beam set can be determined based on the measured beam characteristic parameters of the beam, thereby determining a singular beam with higher accuracy.

[0008] In an optional embodiment, the determining of the singular beam in the entire beam set according to the measurement beam characteristic parameters corresponding to at least one moment includes: for any beam in the entire beam set at any moment, determining the correlation measurement parameter between the beam and the adjacent beam according to the measurement beam characteristic parameters of the beam and the measurement beam characteristic parameters of the adjacent beam corresponding to the beam; and determining whether the beam is the singular beam according to the correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment. In the above scheme, the correlation measurement parameter can be used to characterize the magnitude of the correlation between two objects. Therefore, by determining the correlation measurement parameter between a beam and its adjacent beams, a singular beam in the entire beam set with a low correlation with other beams can be determined more accurately.

[0009] In an alternative embodiment, determining the singular beam in the beam set according to the measurement beam characteristic parameters corresponding to at least one moment further includes: for any beam in the beam set at any moment, determining the significance parameter of the beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam; correspondingly, determining whether the beam is the singular beam according to the correlation metric parameter between the beam and the adjacent beam corresponding to at least one moment includes: determining whether the beam is the singular beam according to the correlation metric parameter between the beam and the adjacent beam corresponding to at least one moment and the significance parameter. In the above solution, the significance parameter can be used to test whether the local numerical spatial autocorrelation is non-randomly significant. Therefore, by determining the significance parameter of the beam, the coincidence of the singular beam determined based on the correlation metric parameter can be reduced, and thus the accuracy of the determined singular beam can be improved.

[0010] In a second aspect, an embodiment of the present application provides a method for reconstructing beam characteristic parameters, which is applied to a network device. The method includes: sending reference signals of each beam in a beam subset to a user terminal, where the beam subset includes singular beams, and the correlation between the singular beams and other beams in the beam set where the beam subset is located is lower than a correlation threshold; receiving the measurement beam characteristic parameters of each beam in the beam subset sent by the user terminal; reconstructing the predicted beam characteristic parameters of other beams in the beam set except the beam subset based on the measurement beam characteristic parameters of each beam in the beam subset.

[0011] In the above solution, in the process of reconstructing the predicted beam characteristic parameters of other beams in the beam set except the beam subset based on the measurement beam characteristic parameters of each beam in the beam subset, singular beams with lower correlation with other beams in the beam set can be added to the beam subset, so that even if the beam characteristic parameters corresponding to the singular beams cannot be obtained through reconstruction, the corresponding beam characteristic parameters can be obtained through measurement. Therefore, the method provided by the embodiment of the present application can obtain the beam characteristic parameters of beams with higher correlation through reconstruction and can also obtain the beam characteristic parameters of beams with lower correlation through measurement, thereby improving the accuracy of the prediction result of the reconstructed beam set.

[0012] In an alternative embodiment, before sending the reference signals of each beam in the beam subset to the user terminal, the method further includes: updating the beam subset based on the beam information corresponding to the singular beam. In the above solution, after receiving the beam information of the singular beam sent by the user terminal, the network device may not add all the singular beams to the beam subset, but update the beam subset according to the beam information of the singular beam, thereby improving the flexibility of updating the beam subset.

[0013] In an optional embodiment, the method further includes: sending reference signals of each beam in the beam set to the user terminal at different times; for any time, receiving measured beam characteristic parameters of each beam in the beam set obtained by the user terminal based on the reference signal sent at that time; and determining the singular beam in the beam set according to the measured beam characteristic parameters corresponding to at least one time. In the above scheme, since the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam set through the measured beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the beam set can be determined based on the measured beam characteristic parameters of the beam, thereby determining a singular beam with higher accuracy.

[0014] In a third aspect, an embodiment of the present application provides a method for determining a beam, comprising: obtaining measurement beam characteristic parameters obtained by measuring a reference signal of each beam in a beam set sent by a user terminal based on a network device at different times; determining a singular beam in the beam set based on the measurement beam characteristic parameters corresponding to at least one moment, wherein the correlation between the singular beam and other beams in the beam set is lower than a correlation threshold.

[0015] In the above scheme, based on the measured beam characteristic parameters of each beam in the entire beam set, a singular beam with low correlation with other beams in the entire beam set can be determined, so that the determined singular beam can be added to the beam subset, so that even if the beam characteristic parameters corresponding to the singular beam cannot be obtained through reconstruction, the corresponding beam characteristic parameters can be obtained through measurement, thereby improving the accuracy of the prediction results of the reconstructed beam set.

[0016] In a fourth aspect, an embodiment of the present application provides a method for reconstructing object feature parameters, comprising: obtaining measured object feature parameters of each object in an object subset, wherein the object subset includes a singular object, and the correlation between the singular object and other objects in the full object set where the object subset is located is lower than a correlation threshold; reconstructing predicted object feature parameters of other objects in the full object set except the object subset based on the measured object feature parameters of each object in the object subset.

[0017] In the above scheme, in the process of reconstructing the predicted object feature parameters of other objects in the full object set except the object subset through the measured object feature parameters of each object in the object subset, a singular object with low correlation with other objects in the full object set can be added to the object subset, so that even if the object feature parameters corresponding to the singular object cannot be obtained through reconstruction, the corresponding object feature parameters can be obtained through measurement. Therefore, the method provided in the embodiment of the present application can obtain the object feature parameters of objects with high correlation through reconstruction, and can also obtain the object feature parameters of objects with low correlation, thereby improving the accuracy of the prediction results of the full object set obtained by reconstruction.

[0018] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus; the processor and the memory communicate with each other through the bus; the memory stores computer program instructions that can be executed by the processor, and the processor calls the computer program instructions to execute the method described in the first aspect, the second aspect, the third aspect or the fourth aspect.

[0019] Specifically, if the electronic device is a user terminal, the electronic device executes the method described in the first aspect, the third aspect or the fourth aspect; if the electronic device is a network side device, the electronic device executes the method described in the second aspect, the third aspect or the fourth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. The following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can also be obtained based on these drawings.

[0021] Figure 1 A flowchart of a beam determination method provided in an embodiment of the present application; Figure 2 A schematic diagram of an observation matrix provided in an embodiment of the present application; Figure 3 A flowchart of a method for reconstructing beam characteristic parameters applied to a user terminal provided in an embodiment of the present application; Figure 4 An interactive diagram of a first beam determination method provided in an embodiment of the present application; Figure 5 A flowchart of a method for reconstructing beam characteristic parameters of a network device provided in an embodiment of the present application; Figure 6 An interactive diagram of a second beam determination method provided in an embodiment of the present application; Figure 7 Block diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0022] When reconstructing the original or target data of the entire set of objects through partial information of a subset of objects, if the prediction results of some objects are inaccurate, the accuracy of the prediction results of the reconstructed objects will be relatively low. Therefore, singular objects with relatively low correlation with other objects in the entire set of objects can be added to the subset of objects to improve the accuracy of the prediction results of the reconstructed objects. In view of this, an embodiment of the present application provides a method for reconstructing object characteristic parameters, and the method may include: Step 1: Obtain the measured object characteristic parameters of each object in the subset of objects; Step 2: Reconstruct the predicted object characteristic parameters of other objects in the entire set of objects except the subset of objects based on the measured object characteristic parameters of each object in the subset of objects.

[0023] Specifically, the subset of objects includes singular objects, and the correlation between the singular objects and other objects in the entire set of objects where the subset of objects is located is lower than the correlation threshold. Therefore, in the process of reconstructing the predicted object characteristic parameters of other objects in the entire set of objects except the subset of objects based on the measured object characteristic parameters of each object in the subset of objects, the object characteristic parameters of objects with relatively high correlation can be obtained through reconstruction, and the object characteristic parameters of objects with relatively low correlation can be directly obtained.

[0024] Among them, before the above Step 1, the step of determining the singular objects in the entire set of objects may be executed. It should be noted that the embodiment of the present application does not make specific limitations on the specific implementation manner of determining the singular objects in the entire set of objects, and those skilled in the art can make appropriate adjustments according to the actual situation. For example, the correlation metric parameters of each object in the entire set of objects can be calculated, and then it can be determined whether the object is a singular object; or, the correlation metric parameters of each object in the entire set of objects can be calculated, and then the significance parameter corresponding to the object can be determined, and then it can be determined whether the object is a singular object, etc.

[0025] In addition, the method of the embodiment of the present application can be applied to a variety of scenarios. For example, the object can be a beam, a user terminal, a sensor, an image pixel point, etc.

[0026] Taking the object as the beam as an example, in beam management, the user terminal needs to determine the optimal beam based on the Received Signal Reference Power (RSRP) of the beam; however, it consumes a lot of resources for the network terminal to send the reference signals of all beams to the user terminal. Therefore, a reconstruction method can be adopted. The network device only needs to send the reference signals corresponding to the beam subset to the user terminal, instead of sending the reference signals corresponding to the entire beam set, thereby reducing its resource overhead. Specifically, the user terminal can receive the reference signals of each beam in the beam subset sent by the network device, and measure the measured beam characteristic parameters (such as RSRP, etc.) of each beam in the beam subset based on the reference signals, and then reconstruct the predicted beam characteristic parameters of the other beams in the entire beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0027] Taking the object as the user terminal as an example, in wireless communication deployment and optimization, the operator hopes to understand the propagation situation of the signals of the network device in space (i.e., the wireless coverage map); however, the measurement points of the user terminal are sparse and uncontrollable, and the channel information of the continuous geographical area cannot be fully observed. Therefore, a reconstruction method can be adopted. The network device only needs to obtain the measured terminal characteristic parameters (such as the user terminal location, Signal-to-Noise Ratio (SNR), etc.) of each terminal in the terminal subset, instead of obtaining the measured terminal characteristic parameters of each terminal in the entire terminal set, thereby enabling the reconstruction of the spatial signal field based on the information reported by a small number of terminals. Specifically, the network device can obtain the measured terminal characteristic parameters of each terminal in the terminal subset, and then reconstruct the predicted terminal characteristic parameters of the other terminals in the entire terminal set except the terminal subset based on the measured terminal characteristic parameters of each terminal in the terminal subset.

[0028] Taking the object as the sensor as an example, in atmospheric environment monitoring and urban perception systems, sensors (such as gas sensors, temperature sensors, humidity sensors, etc.) are deployed at several fixed points in the city. In the case of sparse deployment, most areas in the city are not observed. Therefore, a reconstruction method can be adopted. Only the measured sensor characteristic parameters (such as the observed values of the sensors) of each sensor in the deployed sensor subset need to be obtained, instead of obtaining the measured sensor parameters of each sensor in the entire sensor set, thereby enabling the prediction of the environmental values at other spatial positions based on a small number of known observation points and obtaining a complete pollution heat map or temperature map. Specifically, the electronic device can obtain the measured sensor characteristic parameters of each sensor in the sensor subset, and then reconstruct the predicted sensor characteristic parameters of the other sensors in the entire sensor set except the sensor subset based on the measured sensor characteristic parameters of each sensor in the sensor subset.

[0029] In the above scheme, in the process of reconstructing the predicted object feature parameters of other objects in the full object set except the object subset through the measured object feature parameters of each object in the object subset, a singular object with low correlation with other objects in the full object set can be added to the object subset, so that even if the object feature parameters corresponding to the singular object cannot be obtained through reconstruction, the corresponding object feature parameters can be obtained through measurement. Therefore, the method provided in the embodiment of the present application can obtain the object feature parameters of objects with high correlation through reconstruction, and can also obtain the object feature parameters of objects with low correlation, thereby improving the accuracy of the prediction results of the full object set obtained by reconstruction.

[0030] The following takes the object as a beam as an example to describe in detail the specific implementation of the method for reconstructing the object characteristic parameters. It is understandable that the following specific implementation can be applied to other objects, and those skilled in the art can make corresponding replacements, which will not be described in detail in the embodiments of the present application; for example, the beam set can be replaced with other object sets, and the beam characteristic parameters of the beam can be replaced with object characteristic parameters corresponding to other objects, etc.

[0031] Before introducing the solution provided by this embodiment, the training process of the reconstruction model in beam management is described. Using the trained reconstruction model, the spatial domain downlink beam of beam set A (Set A) can be predicted based on the measurement results of beam set B (Set B).

[0032] Specifically, Set B is a set smaller than Set A (i.e., Set B is a subset of Set A), and both Set B and Set A can be sets of reference signals, for example, Set B can be a set of Channel State Information - Reference Signals (CSI-RS) or a set of Synchronization Signal Blocks (SSB), and Set A can be a set of CSI-RS. For example, Set B includes CSI-RS#[2,4,6,8] (i.e., CSI-RS corresponding to beams with beam identifiers of 2, 4, 6, and 8, respectively), and Set A includes CSI-RS#[1,2,3,4,5,6,7,8] (i.e., CSI-RS corresponding to beams with beam identifiers of 1, 2, 3, 4, 5, 6, 7, and 8, respectively).

[0033] The base station selects Set B from Set A and sends Set B to the user terminal; the terminal measures Set B to obtain the RSRP measurement value of Set B, and uses the above RSRP measurement value as the input or part of the input of the reconstruction model, and uses the reconstruction model to predict the RSRP of Set A; repeating the above training process can obtain a trained reconstruction model. Using the above trained reconstruction model, the RSRP of Set A can be reconstructed based on the RSRP of Set B. If the above reconstruction model is not used, the base station needs to send the entire Set A to the terminal, and its resource overhead will increase; if the above reconstruction model is used, the base station only needs to send Set B, that is, part of Set A, to the terminal, which can reduce its resource overhead.

[0034] For example, Set A includes 64 beams. Through CSI - reportConfig#1 configuration, 4 beams in Set A (i.e., beam {#1, 3, 5, 7}) are selected as Set B. The terminal measures Set B and obtains RSRP {#1,3, 5, 7}, which is then used as the input of the reconstruction model to output the predicted RSRP values of the 8 reference signals in Set A, or output the probability that each reference signal in Set A has the maximum RSRP measurement value in Set A, i.e., the predicted probability.

[0035] Based on the training process of the above reconstruction model and the beam reconstruction process, if there are beams in Set A with low correlation with other beams, in the process of reconstructing the RSRP corresponding to Set A using the RSRP corresponding to Set B, it may be impossible to reconstruct the RSRP corresponding to these beams, resulting in lower accuracy of the reconstructed RSRP corresponding to Set A.

[0036] In view of this, an embodiment of the present application provides a method for reconstructing beam characteristic parameters and a method for determining a beam. The method for reconstructing beam characteristic parameters is used to reconstruct the predicted beam characteristic parameters of each beam in the entire beam set based on the measured beam characteristic parameters of each beam in the beam subset. Different from the beam reconstruction process in the prior art, the beam subset in the method for reconstructing beam characteristic parameters provided in the embodiment of the present application includes a singular beam (i.e., a beam with a low correlation with other beams); the method for determining a beam is used to determine the above-mentioned singular beam.

[0037] It can be understood that the singular beam in the method for reconstructing beam characteristic parameters can be determined by using the beam determination method; and the singular beam determined by the beam determination method can be used in the method for reconstructing beam characteristic parameters, and can also be used for other purposes.

[0038] It should be noted that the above method for reconstructing beam characteristic parameters can be executed either by the user equipment or by the network device. If the method for reconstructing beam characteristic parameters is executed by the user equipment, the reconstruction model is deployed on the user equipment; if the method for reconstructing beam characteristic parameters is executed by the network device, the reconstruction model is deployed on the network device. Similarly, the above method for determining the beam can be executed either by the user equipment or by the network device.

[0039] For example, as a first implementation manner, both the method for reconstructing beam characteristic parameters and the method for determining the beam are executed by the user equipment; as a second implementation manner, both the method for reconstructing beam characteristic parameters and the method for determining the beam are executed by the network device; as a third implementation manner, the method for reconstructing beam characteristic parameters is executed by the user equipment, and the method for determining the beam is executed by the network device; as a fourth implementation manner, the method for reconstructing beam characteristic parameters is executed by the network device, and the method for determining the beam is executed by the user equipment.

[0040] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application.

[0041] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for determining a beam provided by an embodiment of the present application. The method for determining the beam can be executed either by the user equipment or by the network device. The method for determining the beam may include: S101: Obtain the measured beam characteristic parameters of the user equipment based on the reference signals of each beam in the beam set sent by the network device at different times. S102: Determine the singular beams in the beam set according to the measured beam characteristic parameters corresponding to at least one time.

[0042] The beam identifiers of each beam in the beam set are stored in the network device, and the network device may send the reference signals of each beam in the beam set to the user equipment; after receiving the reference signals of each beam in the beam set sent by the network device, the user equipment may obtain the measured beam characteristic parameters of each beam in the beam set based on the above reference signals.

[0043] The embodiments of the present application do not specifically limit the forms of the above reference signals and measured beam characteristic parameters. The measured beam characteristic parameters are used to screen the optimal beam, so any parameters that can reflect whether the beam is optimal can be used, such as RSRP, the arrival angle of the beam, the beam width, etc. The reference signals are used to measure the above measured beam characteristic parameters, so different reference signals can be matched according to different measured beam characteristic parameters, and those skilled in the art can make appropriate adjustments according to the actual situation. For example, the reference signals may include SSB, CSI-RS, etc.

[0044] Optionally, based on the reference signals of each beam in the beam set transmitted by the network device at different times, the terminal can measure different measurement beam characteristic parameters. Among them, the above different times may include the time when the user terminal switches to access the cell, the time when the communication environment changes, the time when the position of the user terminal changes, etc. Therefore, the above beam set may correspond to a set of multiple measurement beam characteristic parameters, and each set includes the measurement beam characteristic parameters measured by the terminal based on the reference signals of each beam in the beam set transmitted by the network device at a certain time.

[0045] If the beam determination method provided in the embodiments of this application is executed by the user terminal, the terminal receives the reference signals of each beam in the beam set transmitted by the network device at different times, and measures the measurement beam characteristic parameters of each beam in the beam set based on the reference signals transmitted at that time for any time, so as to obtain the measurement beam characteristic parameters in S101.

[0046] If the beam determination method provided in the embodiments of this application is executed by the network device, the network device transmits the reference signals of each beam in the beam set to the user terminal at different times, and receives the measurement beam characteristic parameters of each beam in the beam set measured by the user terminal based on the reference signals transmitted at that time for any time, so as to obtain the measurement beam characteristic parameters in S101.

[0047] In S102, a singular beam refers to a beam whose correlation with other beams in the beam set is lower than the correlation threshold. Among them, the embodiments of this application do not specifically limit the form and value of the above correlation threshold. It can be a numerical value or a set of multiple numerical values, and its numerical size can be adjusted according to the actual situation.

[0048] For S102, as an implementation manner, the singular beam in the beam set can be determined based on the measurement beam characteristic parameters corresponding to one time. For example: determine the correlation metric parameter corresponding to the beam based on the measurement beam characteristic parameters corresponding to one time, and then determine whether the beam is a singular beam; or determine the correlation metric parameter of the beam based on the measurement beam parameters corresponding to one time, then determine the significance parameter corresponding to the beam, and then determine whether the beam is a singular beam, etc.

[0049] For S102, as another implementation, singular beams in the beam full set can be determined based on the measurement beam feature parameters corresponding to multiple moments. For example, the correlation metric parameters corresponding to the beams can be determined respectively based on the measurement beam feature parameters corresponding to multiple moments, and the above correlation metric parameters can be statistically analyzed to further determine whether the beam is a singular beam; alternatively, the correlation metric parameters corresponding to the beams can be determined respectively based on the measurement beam feature parameters corresponding to multiple moments, then the significance parameters corresponding to the beams can be determined, and the above significance parameters can be statistically analyzed to further determine whether the beam is a singular beam, etc.

[0050] In the above solution, singular beams with relatively low correlation with other beams in the beam full set can be determined based on the measurement beam feature parameters of each beam in the beam full set. Thus, the determined singular beams can be added to the beam subset, so that even if the beam feature parameters corresponding to the singular beams cannot be obtained through reconstruction, the corresponding beam feature parameters can be obtained through measurement, thereby improving the accuracy of the prediction result of the beam full set obtained by reconstruction.

[0051] Next, four specific implementations for determining singular beams are introduced. First, singular beams are determined using correlation metric parameters; second, singular beams are determined using correlation metric parameters and significance parameters; third, singular beams are determined using correlation metric parameters and beam classification results; fourth, singular beams are determined using correlation metric parameters, significance parameters, and beam classification results.

[0052] Next, the first specific implementation for determining singular beams is introduced, that is, singular beams are determined using correlation metric parameters. At this time, S102 can include: S201: For any beam in the beam full set at any moment, determine the correlation metric parameter between the beam and its adjacent beam according to the measurement beam feature parameter of the beam and the measurement beam feature parameter of the adjacent beam corresponding to the beam. S202: Determine whether the beam is a singular beam according to the correlation metric parameter between the beam and its adjacent beam corresponding to at least one moment.

[0053] In S201, the adjacent beam corresponding to the beam refers to the beam in the beam full set with a relatively small distance from the beam. In the embodiments of the present application, the meaning expressed by the distance between two beams in the beam full set is not specifically limited. For example, the above distance can represent the distance between two beams in the beam matrix, etc. The beam matrix includes multiple beams arranged in a certain order.

[0054] With respect to different distance representation methods, the embodiments of the present application do not impose any specific limitations on the specific implementation methods for determining adjacent beams of a beam. For example, the physical distance between two beams can be calculated using the beam arrival angle and adjacent beams can be found, or a sliding window can be used to find adjacent beams from a beam matrix.

[0055] The correlation measurement parameter between a beam and its adjacent beams is used to characterize the correlation between the beam and its adjacent beams. The embodiment of the present application does not specifically limit the form of the correlation measurement parameter, for example: the correlation measurement parameter can be one of the local Moran index of the beam, the correlation coefficient between beams, and the full beam correlation matrix.

[0056] For different correlation measurement parameters, the embodiments of the present application do not impose any specific restrictions on the specific implementation method of determining the correlation measurement parameters. For example, the correlation measurement parameters can be determined by calculating the local Moran index of the beam, the correlation coefficient between beams, or the full beam correlation matrix.

[0057] It should be noted that S201 may be executed once for each beam in the beam set at a moment, and S201 may be executed multiple times at each moment (the number of executions may be the same as the number of beams in the beam set).

[0058] In S202, as an implementation mode, a singular beam in the entire beam set can be determined based on a correlation measurement parameter corresponding to a moment, for example, a correlation measurement parameter corresponding to the beam can be determined based on a measured beam characteristic parameter corresponding to a moment, and then it is determined whether the beam is a singular beam; as another implementation mode, a singular beam in the entire beam set can be determined based on correlation measurement parameters corresponding to multiple moments, for example, correlation measurement parameters corresponding to the beam can be respectively determined based on measured beam characteristic parameters corresponding to multiple moments, and the above correlation measurement parameters can be statistically analyzed to determine whether the beam is a singular beam.

[0059] The specific implementation of S202 is introduced below by taking an example. Whether the beam is a singular beam can be determined by judging whether the beam satisfies any of the following conditions. If the beam satisfies any of the following conditions, the beam is characterized as a singular beam: Condition 2: The correlation measurement parameter is less than a first threshold, where the correlation threshold is the first threshold.

[0060] Specifically, if the correlation metric parameter is less than the first threshold, it can be considered that the correlation between the beam and the adjacent beam is small, so the beam can be determined as a singular beam. The specific value of the first threshold can be adjusted according to actual conditions. For example, when the correlation metric parameter is a local Moran parameter, the first threshold can be 0.

[0061] It should be noted that when determining the singular beam in the beam full set based on the correlation metric parameter corresponding to a moment, if a certain beam in the beam full set at this moment meets the above condition 2, then this beam can be determined as the singular beam; when determining the singular beam in the beam full set based on the correlation metric parameters corresponding to multiple moments, if a certain beam in the beam full set at any moment meets the above condition 2, then this beam can be determined as the singular beam.

[0062] Condition 9: The number of times that this beam is determined as the first candidate singular beam according to Condition 2 based on multiple measurement beam characteristic parameters is greater than the second threshold, where the correlation threshold includes the second threshold.

[0063] Specifically, the first candidate singular beam means that based on the measurement beam characteristic parameters of the beam at a certain moment, this beam can be determined as the singular beam according to Condition 2, then this beam can be regarded as the first candidate singular beam at this moment. That is to say, for any moment, the correlation metric parameter of the beam can be determined according to the corresponding measurement beam characteristic parameters, and then according to the above Condition 2 and the above correlation metric parameter, it can be determined whether this beam is a singular beam at this moment. If this beam is a singular beam at this moment, then it is determined as the first candidate beam.

[0064] By statistically counting the number of times that the measurement beam characteristic parameters corresponding to multiple moments are determined as the first candidate singular beam according to Condition 2, where the above number being greater than the second threshold can be considered that the correlation between this beam and the adjacent beam is small, so this beam can be determined as the singular beam. Among them, the specific numerical value of the second threshold can be adjusted according to the actual situation.

[0065] In the above solution, the correlation metric parameter can be used to characterize the correlation magnitude between two objects. Therefore, by determining the correlation metric parameter between the beam and its adjacent beam, the singular beam with a relatively low correlation with other beams in the beam full set can be determined more accurately.

[0066] Next, the second specific implementation manner for determining the singular beam is introduced, that is, using the correlation metric parameter and the significance parameter to determine the singular beam. At this time, S102 may include: S301: For any beam in the full beam set at any moment, determine the correlation measurement parameter between the beam and the adjacent beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S302: For any beam in the full beam set at any moment, determine the significance parameter of the beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S303: Determine whether the beam is a singular beam according to the correlation measurement parameter and significance parameter between the beam and the adjacent beam corresponding to at least one moment. The specific implementation of S301 is the same as the specific implementation of S201 and will not be repeated here.

[0067] Optionally, as an implementation, the execution order of S301 and S302 is not limited. As another implementation, S302 is executed after the beam is determined as a candidate beam based on the correlation measurement parameter in S301. In this case, S301 is executed first and then S302.

[0068] In S302, the significance parameter of the beam is used to test whether the local numerical spatial autocorrelation is non-randomly significant. The embodiment of the present application does not specifically limit the specific implementation method of determining the significance parameter, for example: determining the significance parameter based on the significance test of the correlation coefficient, determining the significance parameter based on statistics, etc.

[0069] It should be noted that S302 may be executed once for each beam in the beam set at a moment, and S302 may be executed multiple times at each moment (the number of executions may be the same as the number of beams in the beam set).

[0070] In S303, as an implementation method, a singular beam in the entire beam set can be determined based on the correlation measurement parameters and significance parameters corresponding to a moment, for example: the correlation measurement parameters and significance parameters corresponding to the beam are determined based on the measured beam characteristic parameters corresponding to a moment, and then it is determined whether the beam is a singular beam; as another implementation method, a singular beam in the entire beam set can be determined based on the correlation measurement parameters and significance parameters corresponding to multiple moments, for example: the correlation measurement parameter significance parameters corresponding to the beam can be respectively determined based on the measured beam characteristic parameters corresponding to multiple moments, and the above correlation measurement parameters and significance parameters are statistically analyzed to determine whether the beam is a singular beam.

[0071] The specific implementation of S303 is introduced below by taking an example. Whether the beam is a singular beam can be determined by judging whether the beam satisfies any of the following conditions. If the beam satisfies any of the following conditions, the beam is characterized as a singular beam: Condition Six: The correlation metric parameter is less than the first threshold, and the significance parameter is less than the fourth threshold, where the correlation thresholds include the first threshold and the fourth threshold.

[0072] Specifically, if the correlation metric parameter is less than the first threshold, it can be considered that the correlation between this beam and the adjacent beam is small. And if the significance parameter is less than the fourth threshold, it can be considered that the credibility of the small correlation between this beam and the adjacent beam is high. Therefore, this beam can be determined as a singular beam. Among them, the specific numerical values of the first threshold and the third threshold can both be adjusted according to the actual situation, and there is no association between the specific numerical values of the first threshold and the third threshold.

[0073] Optionally, when determining the singular beams in the beam set based on the correlation metric parameter and the significance parameter corresponding to one moment, if a certain beam in the beam set at this moment meets the above Condition Six, then this beam can be determined as a singular beam; when determining the singular beams in the beam set based on the correlation metric parameter and the significance parameter corresponding to multiple moments, if a certain beam in the beam set at any moment meets the above Condition Six, then this beam can be determined as a singular beam.

[0074] Condition Ten: The number of times this beam is determined as a first candidate singular beam according to Condition Six based on multiple measured beam characteristic parameters is greater than the fifth threshold, where the correlation thresholds include the fifth threshold.

[0075] Specifically, the first candidate singular beam means that based on the measured beam characteristic parameters of the beam at a certain moment, this beam can be determined as a singular beam according to Condition Six, then this beam can be regarded as a first candidate singular beam at this moment. That is to say, for any moment, the correlation metric parameter and the significance parameter of the beam can be determined according to the corresponding measured beam characteristic parameters, and then according to the above Condition Six, the above correlation metric parameter and the above significance parameter, it can be determined whether this beam is a singular beam at this moment. If this beam is a singular beam at this moment, then it is determined as a first candidate beam.

[0076] Count the number of times the measured beam characteristic parameters corresponding to multiple moments are determined as the first candidate singular beam according to Condition Six. Among them, if the above number is greater than the fifth threshold, it can be considered that the correlation between this beam and the adjacent beam is small. Therefore, this beam can be determined as a singular beam. Among them, the specific numerical value of the fifth threshold can be adjusted according to the actual situation.

[0077] In the above solution, the significance parameter can be used to test whether the local numerical spatial autocorrelation is non-randomly significant. Therefore, by determining the significance parameter of the beam, the coincidence of the singular beams determined based on the correlation metric parameter can be reduced, and thus the accuracy of the determined singular beams can be improved.

[0078] Then, a third specific implementation manner for determining a singular beam is introduced, that is, determining a singular beam by using a correlation metric parameter and a beam classification result. At this time, S102 may include: S401, for any beam in the beam set at any moment, determine the correlation metric parameter between the beam and its adjacent beam according to the measured beam feature parameter of the beam and the measured beam feature parameter of the adjacent beam corresponding to the beam. S402, for any moment, determine the beam classification result of the beam according to the correlation metric parameter of the beam and the correlation metric parameters of other beams in the beam set. S403, determine whether the beam is a singular beam according to the beam classification result and the correlation metric parameter of the beam.

[0079] The specific implementation manner of S401 is the same as that of S201, and will not be elaborated here. In S402, the embodiments of the present application do not specifically limit the classification categories corresponding to the beam classification result. For example, the beam classification result may include singular beams and non-singular beams, or may include severely singular beams, moderately singular beams, and mildly singular beams, etc.

[0080] In addition, the embodiments of the present application do not specifically limit the specific implementation manner of determining the above beam classification result. Optionally, the mean value and variance may be calculated based on the correlation metric parameter, and the beam classification result may be determined by comparing the correlation metric parameter of the beam with the above mean value and variance; or, the distribution of the correlation metric parameter may be determined, and the beam classification result may be determined based on the correlation metric parameter of the beam and the above distribution, etc.

[0081] It should be noted that S402 can be executed once for each moment.

[0082] Next, an example is given to introduce the specific implementation manner of S403. It can be determined whether the beam is a singular beam by determining whether the beam satisfies any of the following conditions. Among them, if the beam satisfies any of the following conditions, it indicates that the beam is a singular beam: Condition 1: The beam classification result indicates that the beam is a singular beam.

[0083] Condition 2: For any moment, the correlation metric parameter is less than the first threshold, where the correlation threshold is the first threshold.

[0084] It should be noted that when determining the singular beams in the beam set based on the correlation metric parameters and beam classification results corresponding to a moment, if a certain beam in the beam set at this moment meets the above condition two, then this beam can be determined as a singular beam; when determining the singular beams in the beam set based on the correlation metric parameters and beam classification results corresponding to multiple moments, if a certain beam in the beam set at any moment meets the above condition two, then this beam can be determined as a singular beam.

[0085] Condition three: The number of times that this beam is determined as a first candidate singular beam according to condition one or condition two in multiple moments is greater than a second threshold, where the correlation threshold includes the second threshold.

[0086] Condition four: The sum of the number weight determined based on the beam classification result and the number of times that this beam is determined as a second candidate singular beam according to condition three is greater than a third threshold, where the correlation threshold includes the third threshold.

[0087] Specifically, the second candidate singular beam means that if a beam can be determined as a first candidate beam according to condition one or condition two, then this beam can be regarded as a second candidate beam. That is to say, for any moment, it can be determined whether this beam is a singular beam at this moment according to the corresponding measured beam characteristic parameters. If this beam is a singular beam at this moment, then it is determined as a second candidate beam.

[0088] Statistically count the number of times that the measured beam characteristic parameters corresponding to multiple moments are determined as third candidate singular beams according to condition three, and the number that can be configured for the beam based on the beam classification result corresponding to the beam. If the sum of the above two numbers is greater than the third threshold, it can be considered that the correlation between this beam and the adjacent beams is small. Therefore, this beam can be determined as a singular beam. The specific numerical value of the third threshold can be adjusted according to the actual situation.

[0089] In the above solution, after determining the first candidate singular beam based on the correlation metric parameter for any moment, the occasionality can be reduced by statistically counting the number of times of determining the first candidate singular beam in multiple moments, thereby improving the accuracy of the determined singular beam. In addition, by classifying the singular beams, the method for determining the singular beams can be further optimized according to user requirements, thereby improving the adaptability of the determined singular beams.

[0090] Next, the fourth specific implementation manner for determining the singular beam is introduced, that is, using the correlation metric parameter, the significance parameter, and the beam classification result to determine the singular beam. At this time, S102 may include: S501. For any beam in the beam set at any moment, determine the correlation measurement parameter between the beam and the adjacent beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S502. For any beam in the beam set at any moment, determine the significance parameter of the beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S503. For any moment, determine the beam classification result of the beam according to the correlation measurement parameter of the beam and the correlation measurement parameters of other beams in the beam set. S504. Determine whether the beam is a singular beam according to the beam classification result, the correlation measurement parameter of the beam and the significance parameter.

[0091] The specific implementation of S501 is the same as that of S201, the specific implementation of S502 is the same as that of S302, and the specific implementation of S503 is the same as that of S402, which will not be repeated here.

[0092] The specific implementation of S504 is introduced below by taking an example. Whether the beam is a singular beam can be determined by judging whether the beam satisfies any of the following conditions. If the beam satisfies any of the following conditions, it is characterized that the beam is a singular beam: Condition 5: The beam classification result indicates that the beam is a singular beam.

[0093] Condition six: at any moment, the correlation measurement parameter is less than the first threshold, and the significance parameter is less than the fourth threshold, wherein the correlation threshold includes the first threshold and the fourth threshold.

[0094] It should be noted that when determining the singular beam in the entire beam set based on the correlation measurement parameters, significance parameters and beam classification results corresponding to a moment, if a beam in the entire beam set at that moment satisfies the above condition six, then the beam can be determined as a singular beam; when determining the singular beam in the entire beam set based on the correlation measurement parameters, significance parameters and beam classification results corresponding to multiple moments, if a beam in the entire beam set at any moment satisfies the above condition six, then the beam can be determined as a singular beam.

[0095] Condition seven: the number of times that the beam is determined as the first candidate singular beam according to condition five or condition six at multiple time moments is greater than a fifth threshold, wherein the correlation threshold includes the fifth threshold.

[0096] Condition eight: the sum of the number of times weight determined based on the beam classification result and the number of times the beam is determined as the second candidate singular beam according to condition seven is greater than a sixth threshold, wherein the correlation threshold includes the sixth threshold.

[0097] Specifically, the second candidate singular beam means that if a beam can be determined as the first candidate beam according to Condition Five or Condition Six, then this beam can be regarded as the second candidate beam. That is to say, for any moment, it is possible to determine whether this beam is a singular beam at this moment according to the corresponding measured beam characteristic parameters. If this beam is a singular beam at this moment, then it is determined as the second candidate beam.

[0098] Statistically count the number of times that the measured beam characteristic parameters corresponding to multiple moments are determined as the third candidate singular beam according to Condition Seven, and, based on the beam classification result corresponding to the beam, the corresponding number of times can be configured for the beam. Among them, if the sum of the above two numbers of times is greater than the third threshold, it can be considered that the correlation between this beam and the adjacent beam is small. Therefore, this beam can be determined as a singular beam. The specific numerical value of the third threshold can be adjusted according to the actual situation.

[0099] In the above solution, after determining the first candidate singular beam based on the correlation metric parameter and the significance parameter for any moment, the accidental occurrence can be reduced by statistically counting the number of times the first candidate singular beam is determined in multiple moments, thereby improving the accuracy of the determined singular beam. In addition, by classifying the singular beams, the method for determining the singular beams can be further optimized according to user requirements, thereby improving the adaptability of the determined singular beams.

[0100] Further, on the basis of the above embodiments, the correlation metric parameter can be the local Moran's I.

[0101] In the above solution, the local Moran's I is an important index in spatial statistical analysis for measuring the local spatial correlation between a spatial unit and its neighbors. Among them, the above local space is the matrix space in the observation matrix. By determining the local Moran's I corresponding to the beam in the above matrix space, a correlation metric parameter with relatively high accuracy can be obtained.

[0102] Taking the correlation metric parameter as the local Moran's I as an example, the specific implementation manner of determining the significance parameter in the above embodiments will be introduced below. That is, S302 or S502 may include: S601: Execute in a loop: Randomly select a beam from this beam and its adjacent beams as the central beam, and the other beams as the adjacent beams of the central beam, and calculate the new local Moran's I corresponding to the central beam; until the set number of loops is reached. S602: Determine the significance parameter according to the local Moran's I of this beam and the new local Moran's I in the number of loops.

[0103] In S601, the specific implementation of determining the adjacent beams of the central beam is the same as that of determining the adjacent beams of any beam in the above embodiment, and the specific implementation of calculating the new local Moran's index corresponding to the central beam is also the same as that of calculating the local Moran's index of any beam in the above embodiment. Therefore, it will not be elaborated here.

[0104] Among them, the specific numerical value of the number of cycles can be adjusted according to the actual situation. For example, the number of cycles can be 999.

[0105] In S602, the significance parameter can be determined by counting the number of parameters whose absolute value of the new local Moran's index is greater than the absolute value of the local Moran's index of the beam among the number of cycles of the new local Moran's indices, and then taking the quotient of the number of parameters and the number of cycles.

[0106] In the above solution, by randomly selecting the central beam and calculating its corresponding local Moran's index, it can be verified whether the low correlation between this beam and other beams is a random phenomenon, thereby reducing the coincidence of the singular beams determined based on the correlation metric parameters, and further improving the accuracy of the determined singular beams.

[0107] The following introduces the specific implementation of determining the beam classification result in the above embodiment. That is, S402 or S502 may include: S701: Obtain the mean and variance of the correlation metric parameter of this beam and the correlation metric parameters of other singular beams. S702: Determine the beam classification result according to the magnitude relationship between the correlation metric parameter of this beam and the mean and variance.

[0108] Specifically, different beam classification results in S702 represent different magnitudes of the correlation between this beam and other beams in the beam set. For example: If the beam classification results include severely singular beams, moderately singular beams, and mildly singular beams, then the severely singular beams represent that the correlation between this beam and other beams in the beam set is extremely low, the moderately singular beams represent that the correlation between this beam and other beams in the beam set is relatively low, and the mildly singular beams represent that the correlation between this beam and other beams in the beam set is slightly low. Different classifications can be assigned different frequency weights, and the closer the classification is to the singular beam, the greater the frequency weight.

[0109] The following gives an example to introduce an implementation of determining the beam classification result according to the magnitude relationship between the correlation metric parameter and the mean and variance. Please refer to Table 1. Table 1 shows the beam classification result, where represents the correlation metric parameter of this beam, represents the mean of the correlation metric parameter of this beam and the correlation metric parameters of other singular beams, Represents the variance of the correlation metric parameter of this beam and the correlation metric parameters of other singular beams.

[0110]

[0111] Table 1 Beam Classification Results Furthermore, for Conditions 1 to 8 in the above embodiments: For Condition 1 and Condition 5, the above three beam classification results (severely singular beam, moderately singular beam, and mildly singular beam) all characterize the beam as a singular beam. Therefore, this beam can be determined as a singular beam; in this example, if the singular beam is not directly determined based on the beam classification result, then this beam is not a singular beam; for Condition 2, in addition to the above beam classification results, in other classification examples, it is necessary to further determine whether the correlation metric parameter of the beam is less than the first threshold to determine whether this beam is a singular beam; for Condition 3, based on the above beam classification results, it is necessary to further determine whether the number of times the beam is determined as the first candidate singular beam is greater than the second threshold to determine whether this beam is a singular beam; for Condition 4, it can be further determined whether the sum of the above-mentioned number weight and the number of times the beam is determined as the second candidate singular beam is greater than the third threshold to determine whether this beam is a singular beam; for Condition 6, in addition to the above beam classification results, in other classification examples, it is necessary to further determine whether the correlation metric parameter of the beam is less than the first threshold and whether the significance parameter is less than the fourth threshold to determine whether this beam is a singular beam; for Condition 7, based on the above beam classification results, it is necessary to further determine whether the number of times the beam is determined as the first candidate singular beam is greater than the fifth threshold to determine whether this beam is a singular beam; for Condition 8, it can be further determined whether the sum of the above-mentioned number weight and the number of times the beam is determined as the second candidate singular beam is greater than the sixth threshold to determine whether this beam is a singular beam.

[0112] In the above solution, the singular beams can be classified based on the mean and variance of the correlation metric parameters. Since the mean and variance of the correlation metric parameters reflect the distribution of the correlation metric parameters of each beam, beams with extremely low and slightly low correlations can be distinguished, thereby improving the accuracy of the determined singular beams.

[0113] The specific implementation manner of determining the adjacent beam corresponding to the beam is introduced below. That is, before S102, the method for reconstructing the beam characteristic parameters provided by the embodiments of the present application may further include: S801: For any beam in the beam set, determine the measured beam characteristic parameters of the neighbor beam corresponding to this beam from the observation matrix based on the set sliding window. S802: Determine the measured beam characteristic parameters of the adjacent beam from the measured beam characteristic parameters of the neighbor beam based on the set adjacency indication function.

[0114] In S801, the observation matrix includes the measurement beam characteristic parameters of each beam in the beam full set, and the sliding window is used to determine the measurement beam characteristic parameters of the neighbor beams corresponding to any beam from the above-mentioned observation matrix. Among them, any beam can be used as the central beam, and the other beams within the sliding window are used as the neighbor beams corresponding to this beam.

[0115] As an implementation, the sliding window can be implemented using a 3×3 matrix, that is: .

[0116] In S802, the adjacency indication function is used to determine whether the neighbor beams corresponding to a beam belong to the adjacent beams corresponding to the beam. As an implementation, the adjacency rule can be set to meet the following conditions: ; Among them, represents the adjacency set of the position , that is, the neighbors in 8 directions including up, down, left, right, and diagonals.

[0117] Then the adjacency indication function can be expressed as: ; Among them, represents the adjacency indication function, which is used to determine whether the position is a neighbor of.

[0118] Optionally, based on the adjacency indication function in the above example, the neighbor beams in 8 directions including up, down, left, right, and diagonals corresponding to the beam will be determined as adjacent beams; in other cases, only some of the neighbor beams in 8 directions can be determined as adjacent beams.

[0119] In the above solution, based on the set sliding window, the neighbor beams are determined from the observation matrix, and based on the set adjacency indication function, the adjacent beams are determined from the neighbor beams, so that the adjacent beams of the beam can be quickly found from the beam full set, and then it is convenient to determine the correlation metric parameter corresponding to the beam.

[0120] Optionally, on the basis of the above embodiment, for the beams located at the edge of the observation matrix, their adjacent beams include supplementary beams and the beams within the sliding window in the observation matrix, and the measurement beam characteristic parameters of the supplementary beams are the mean values of the measurement beam characteristic parameters of the neighbor beams in the sliding window.

[0121] In the above scheme, for the beam located at the edge of the observation matrix, since the number of its neighbor beams located in the sliding window is less than the number of neighbor beams of other beams, the number of neighbor beams of each beam can be made consistent by adding supplementary beams, and the accuracy of the correlation measurement parameters of the beam located at the edge of the observation matrix can be improved.

[0122] Please refer to Figure 2 , Figure 2 A schematic diagram of an observation matrix provided in an embodiment of the present application, Figure 2 The solid circles in the figure represent supplementary beams, the hollow circles represent beams in the full beam set, the center numbers represent the beam identifiers corresponding to the beams, the dotted boxes represent sliding windows, and the arrows represent the sliding directions.

[0123] by Figure 2 Taking the observation matrix shown as an example, and assuming that the measured beam characteristic parameter is RSRP and the correlation measurement parameter is the local Moran index, the specific calculation process of a method for reconstructing beam characteristic parameters provided in an embodiment of the present application is introduced below.

[0124] Beam Complete Collection The corresponding observation matrix It can be expressed as: ; in, express The middle beam is identified as The RSRP of the beam located in the matrix OK List, represents the number of rows of the observation matrix, Indicates the number of columns in the observation matrix.

[0125] Based on sliding window , for the above observation matrix Any position in , extract is the sub-matrix of the center beam , the submatrix The center beam RSRP and the center beam The RSRP of the corresponding neighbor beam.

[0126] Based on the set adjacency indicator function Determine the center beam from the RSRP of the neighbor beams above The RSRP of the adjacent beams, where the center beam The RSRP of the beam and its adjacent beam can be expressed as : 。

[0127] The local Moran's index can be calculated using the following formula: ; where represents the local Moran's index corresponding to the beam at position , represents the RSRP corresponding to this beam, represents the mean of the RSRP of this beam and the RSRP of the adjacent beams corresponding to this beam: ; represents the variance of the RSRP of this beam and the RSRP of the adjacent beams corresponding to this beam: ; represents in the weight of the position.

[0128] For the beams with a local Moran's index less than 0, to verify the statistical significance of its local Moran's index , a non-parametric permutation test method based on the calculation window can be adopted. When performing the significance test, the central point and all the values in its neighborhood are jointly formed into the set to be permuted , and in each permutation, a value is randomly selected as the center, and the rest are used as the neighborhood, so as to calculate the local Moran's index under the permutation: ; where represents the RSRP of the beam at position after the permutation, , represents the th cycle and the value range of is ,

[0129] Then, the empirical P-value, a statistic of the extreme degree of the original local Moran's index during the permutation process, is calculated through the indicator function: ; where represents the indicator function, which takes the value of 1 when the condition is satisfied and 0 otherwise.

[0130] The final significance result can be expressed as: ; where is the significance level threshold (usually 0.05). If the result is 1, the local Moran index at this position is significant, and the beam can be considered as the first candidate singular beam.

[0131] Defining a Collection , add the beam identifier corresponding to the beam that passes the saliency judgment to the set middle: ; statistics The corresponding sets at multiple times , get multiple sets , count the number of times each beam identifier appears. If the number of times corresponding to a certain beam is greater than the third threshold, the beam can be considered as the second candidate singular beam.

[0132] Suppose the set of local Moran indices of the singular beam is , defined as follows: ; Calculate the mean of the local Moran index corresponding to all singular beams With variance : ; ; in, For collection The total number of elements.

[0133] The classification method is shown in Table 1. When a beam is judged as a severe singular beam, its frequency weight is given is 5, which means that the beam is judged as a singular beam five times. And so on, the number of times the beam is finally determined The number of times the beam is determined as the second candidate singular beam When the sum is greater than a sixth threshold, the beam is determined as a singular beam.

[0134] Please refer to Figure 3 , Figure 3 A flowchart of a method for reconstructing beam characteristic parameters applied to a user terminal provided in an embodiment of the present application, in which case the reconstruction model is deployed on the user terminal. The method for reconstructing beam characteristic parameters may include: S901: receiving a reference signal of each beam in a beam subset sent by a network device. S902: measuring the measured beam characteristic parameters of each beam in the beam subset based on the reference signal. S903: reconstructing the predicted beam characteristic parameters of other beams in the entire beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0135] The network device stores the beam identifier of each beam in the beam subset, and the network device can send a reference signal of each beam in the beam subset to the user terminal, wherein the beam subset includes a singular beam whose correlation with other beams is lower than a correlation threshold. After receiving the reference signal of each beam in the beam subset sent by the network device, the user terminal obtains the measured beam characteristic parameter of each beam in the beam subset based on the reference signal.

[0136] In S903, the terminal may reconstruct the predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set based on the measured beam characteristic parameters of each beam in the beam subset. Specifically, the measured beam characteristic parameters of each beam in the beam subset may be input into the reconstruction model to obtain the predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set output by the reconstruction model. In some cases, the output of the reconstruction model may include not only the predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set, but also the predicted beam characteristic parameters of the beam subset.

[0137] In the above scheme, when reconstructing the predicted beam characteristic parameters of other beams in the beam set except the beam subset through the measured beam characteristic parameters of each beam in the beam subset, a singular beam with low correlation with other beams in the beam set can be added to the beam subset, so that even if the beam characteristic parameters corresponding to the singular beam cannot be obtained through reconstruction, the corresponding beam characteristic parameters can be obtained through measurement. Therefore, the method provided in the embodiment of the present application can obtain the beam characteristic parameters of beams with high correlation through reconstruction, and can also obtain the beam characteristic parameters of beams with low correlation through measurement, thereby improving the accuracy of the prediction results of the reconstructed beam set.

[0138] Optionally, based on the above embodiment, the method for reconstructing beam characteristic parameters provided in the embodiment of the present application may further include the step of determining a singular beam. As an implementation mode, after the user terminal establishes a connection with the network device, the step of determining a singular beam may be performed, and the step of reconstructing beam characteristic parameters may be performed when data transmission is required. In this case, the step of determining a singular beam is performed before the step of reconstructing beam characteristic parameters.

[0139] As another implementation, the step of determining the singular beam can be performed periodically or when needed (e.g., when there are significant changes in the communication environment). At this time, the step of determining the singular beam can be performed before or after the step of reconstructing the beam characteristic parameters. If the step of determining the singular beam is performed before the step of reconstructing the beam characteristic parameters, the step of reconstructing the beam characteristic parameters can be performed based on the newly determined singular beam. If the step of determining the singular beam is performed after the step of reconstructing the beam characteristic parameters, the next step of reconstructing the beam characteristic parameters can be performed based on the newly determined singular beam.

[0140] The method for reconstructing beam characteristic parameters provided by the embodiments of the present application may further include: S1001: Receive the reference signals of each beam in the beam subset sent by the network device at different times. S1002: For any given time, measure the measured beam characteristic parameters of each beam in the beam subset based on the reference signals sent at that time. S1003: Determine the singular beam in the beam subset according to the measured beam characteristic parameters corresponding to at least one time. S1004: Send the beam information corresponding to the singular beam to the network device.

[0141] The specific implementation manners of S1001 and S1002 are the same as those of S101, and the specific implementation manner of S1003 is the same as that of S102, which will not be elaborated here.

[0142] In S1004, the embodiments of the present application do not specifically limit the form of the above beam information. For example, the beam information may include the beam identifier of the singular beam, the number of times the singular beam is determined as a candidate singular beam, the correlation metric parameter of the singular beam, etc.

[0143] As an implementation manner, the terminal can send the beam information corresponding to the singular beam to the network device through signaling. For example, the following two variables can be added to the existing signaling: reportQuantity = CRI-RSRP-Odd and numberOfOddBeams = M, where reportQuantity = CRI-RSRP-Odd is used to send the beam identifier corresponding to the singular beam and the number of times the singular beam is determined as a candidate singular beam, and numberOfOddBeams = M is used to send the number of singular beams.

[0144] In the above solution, since the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the entire beam set through the measured beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the beam subset can be determined based on the measured beam characteristic parameters of the beam, and then a singular beam with relatively high accuracy can be determined.

[0145] Further, based on the above embodiments, S903 may include: inputting the measurement beam feature parameters of each beam in the beam subset into a pre-trained reconstruction model to obtain predicted beam feature parameters, where the arrangement order of the beams in the observation matrix is the same as that of the beams in the beam matrix during the training of the reconstruction model.

[0146] Among them, the implementation manner corresponding to the observation matrix has been introduced in detail in the above embodiments and will not be elaborated here.

[0147] In the above solution, since the purpose of the method provided in the embodiments of the present application is to find the beams in the beam subset that may not be obtained due to low correlation during the reconstruction process, by setting the arrangement order of the beams in the observation matrix to be the same as that of the beams in the beam matrix during the training of the reconstruction model, the adjacent beams of the beams in the two processes are the same, thereby improving the accuracy of the determined singular beams.

[0148] Further, based on the above embodiments, a singular beam discriminator (for determining singular beams) and a Moran's I calculator (for calculating local Moran's I) may be deployed on the user terminal; an optimized pattern generator (for updating the beam subset) may be deployed on the network device.

[0149] Please refer to Figure 4 , Figure 4 , which is an interaction diagram of the first beam determination method provided in the embodiments of the present application. The beam determination method may include: S1101: The network device sends the reference signals of each beam in the beam subset to the user terminal at different times. S1102: The user terminal receives the reference signals of each beam in the beam subset sent by the network device at different times. S1103: For any moment, the user terminal measures the measurement beam feature parameters of each beam in the beam subset based on the reference signal sent at that moment. S1104: The user terminal determines the singular beams in the beam subset according to the measurement beam feature parameters corresponding to at least one moment. S1105: The user terminal sends the beam information corresponding to the singular beams to the network device. S1106: The network device receives the beam information corresponding to the singular beams sent by the user terminal. S1107: The network device updates the beam subset based on the beam information corresponding to the singular beams. S1108: The network device sends the reference signals of each beam in the beam subset to the user terminal. S1109: The user terminal receives the reference signals of each beam in the beam subset sent by the network device. S1110: The user terminal measures the measurement beam feature parameters of each beam in the beam subset based on the reference signal. S1111: The user terminal reconstructs the predicted beam feature parameters of the other beams in the beam subset except the beam subset based on the measurement beam feature parameters of each beam in the beam subset.

[0150] Please refer toFigure 5 , Figure 5 A flowchart of a method for reconstructing beam characteristic parameters applied to a network device provided in an embodiment of the present application, in which case the reconstruction model is deployed on the network device. The method for reconstructing beam characteristic parameters may include: S1201: sending a reference signal of each beam in a beam subset to a user terminal. S1202: receiving measured beam characteristic parameters of each beam in a beam subset sent by a user terminal. S1203: reconstructing predicted beam characteristic parameters of other beams in the entire beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0151] Specifically, the network device stores the beam identifier of each beam in the beam subset, and the network device can send a reference signal of each beam in the beam subset to the user terminal, wherein the beam subset includes a singular beam whose correlation with other beams is lower than a correlation threshold. After receiving the reference signal of each beam in the beam subset sent by the network device, the user terminal obtains the measured beam characteristic parameter of each beam in the beam subset based on the reference signal, and sends the measured beam characteristic parameter of each beam in the beam subset to the network device.

[0152] In S1203, the network device may reconstruct predicted beam characteristic parameters of other beams in the entire beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset. Specifically, the measured beam characteristic parameters of each beam in the beam subset may be input into the reconstruction model to obtain predicted beam characteristic parameters of other beams in the entire beam set except the beam subset output by the reconstruction model.

[0153] In the above scheme, in the process of reconstructing the predicted beam characteristic parameters of other beams in the beam set except the beam subset by using the measured beam characteristic parameters of each beam in the beam subset, a singular beam with low correlation with other beams in the beam set can be added to the beam subset, so that even if the beam characteristic parameters corresponding to the singular beam cannot be obtained by reconstruction, the corresponding beam characteristic parameters can be obtained by measurement. Therefore, the method provided in the embodiment of the present application can obtain the beam characteristic parameters of beams with high correlation by reconstruction, and can also obtain the beam characteristic parameters of beams with low correlation by measurement, thereby improving the accuracy of the prediction results of the reconstructed beam set.

[0154] Optionally, based on the above embodiment, before S1201, the method for reconstructing beam characteristic parameters provided in the embodiment of the present application may further include: updating the beam subset based on beam information corresponding to the singular beam.

[0155] Specifically, as an implementation manner, the network device may immediately update the beam subset after receiving the beam information corresponding to the singular beam; as another implementation manner, the network device may also not immediately update the beam subset after receiving the beam information corresponding to the singular beam, but update it before sending the beam subset to the user terminal.

[0156] The embodiments of the present application do not specifically limit the specific implementation manner of updating the beam subset. For example, when there is no singular beam in the original beam subset, the singular beam may be added to the beam subset; or, if the beam that was determined to be added to the beam subset last time is not determined to be a singular beam this time, the beam may be deleted from the beam subset, etc.

[0157] In the above solution, after receiving the beam information of the singular beam sent by the user terminal, the network device may not add all the singular beams to the beam subset, but update the beam subset according to the beam information of the singular beam, thereby improving the flexibility of updating the beam subset.

[0158] Taking the beam information including the beam identifier and the beam occurrence times as an example, the specific implementation manner of updating the beam subset is introduced below. At this time, the above step of updating the beam subset based on the beam information corresponding to the singular beam may include: if the beam occurrence times is greater than the seventh threshold, add the beam identifier of the singular beam to the beam subset.

[0159] The beam occurrence times represents the number of times the singular beam is determined to be a candidate singular beam. If the above beam occurrence times is greater than the seventh threshold, it can be considered that the correlation between the singular beam and other beams is relatively low. Therefore, the beam identifier of the singular beam may be added to the beam subset.

[0160] It should be noted that the specific value of the seventh threshold can be adjusted according to the actual situation. As an implementation manner, the seventh threshold may be determined according to the communication environment information, where the communication environment information includes at least one of the following: user speed, channel state change, multipath effect, and whether there is occlusion.

[0161] In the above solution, for the beam that has been determined to be a singular beam, it can be further determined whether to add the beam identifier corresponding to the beam to the beam subset based on the size of the beam occurrence, thereby improving the flexibility of adding singular beams.

[0162] Further, based on the above embodiments, the method for reconstructing beam characteristic parameters provided by the embodiments of the present application may further include the step of determining a singular beam. As an implementation manner, after the user terminal establishes a connection with the network device, the step of determining a singular beam can be executed, and the step of reconstructing beam characteristic parameters can be executed when data transmission is required. At this time, the step of determining a singular beam is executed before the step of reconstructing beam characteristic parameters.

[0163] As another implementation manner, the step of determining a singular beam can be executed periodically, or can be executed when needed (such as when the communication environment changes greatly). At this time, the step of determining a singular beam can be executed before or after the step of reconstructing beam characteristic parameters. If the step of determining a singular beam is executed before the step of reconstructing beam characteristic parameters, the step of reconstructing beam characteristic parameters can be executed based on the newly determined singular beam; if the step of determining a singular beam is executed after the step of reconstructing beam characteristic parameters, the next step of reconstructing beam characteristic parameters can be executed based on the newly determined singular beam.

[0164] The method for reconstructing beam characteristic parameters provided by the embodiments of the present application may further include: S1301: Sending reference signals of each beam in the beam full set to the user terminal at different times. S1302: For any moment, receiving the measured beam characteristic parameters of each beam in the beam full set measured by the user terminal based on the reference signals sent at this moment. S1303: Determining the singular beam in the beam full set according to the measured beam characteristic parameters corresponding to at least one moment.

[0165] The specific implementation manners of S1301 and S1302 are the same as those of S101, and the specific implementation manner of S1303 is the same as that of S102, and will not be elaborated here.

[0166] In the above solution, since the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam full set through the measured beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the beam full set can be determined based on the measured beam characteristic parameters of the beam, and then a singular beam with relatively high accuracy can be determined.

[0167] Please refer to Figure 6 , Figure 6Interaction diagram of the second beam determination method provided by the embodiments of this application. The beam determination method may include: S1401: The network device sends reference signals of each beam in the beam set to the user terminal at different times. S1402: The user terminal receives the reference signals of each beam in the beam set sent by the network device at different times. S1403: For any moment, the user terminal measures the measurement beam characteristic parameters of each beam in the beam set based on the reference signal sent at this moment. S1404: The user terminal sends the measurement beam characteristic parameters of each beam in the beam subset corresponding to this moment to the network device. S1405: The network device receives the measurement beam characteristic parameters of each beam in the beam subset corresponding to this moment sent by the user terminal. S1406: The network device determines the singular beam in the beam set according to the measurement beam characteristic parameters corresponding to at least one moment. S1407: The network device updates the beam subset based on the beam information corresponding to the singular beam. S1408: The network device sends reference signals of each beam in the beam subset to the user terminal. S1409: The user terminal receives the reference signals of each beam in the beam subset sent by the network device. S1410: The user terminal measures the measurement beam characteristic parameters of each beam in the beam subset based on the reference signal. S1411: The user terminal sends the measurement beam characteristic parameters of each beam in the beam subset to the network device. S1412: The network device receives the measurement beam characteristic parameters of each beam in the beam subset sent by the user terminal. S1413: The network device reconstructs the predicted beam characteristic parameters of the other beams in the beam set except the beam subset based on the measurement beam characteristic parameters of each beam in the beam subset.

[0168] Please refer to Figure 7 , Figure 7 Block diagram of the structure of an electronic device provided by the embodiments of this application. The electronic device 1500 includes: at least one processor 1501, at least one communication interface 1502, at least one memory 1503, and at least one communication bus 1504. Among them, the communication bus 1504 is used to realize the direct connection communication of these components. The communication interface 1502 is used to communicate signaling or data with other node devices. The memory 1503 stores machine-readable instructions executable by the processor 1501. When the electronic device 1500 runs, the processor 1501 communicates with the memory 1503 through the communication bus 1504. When the machine-readable instructions are called by the processor 1501, the above method is executed.

[0169] As an implementation manner, the above-mentioned electronic device 1500 may be a user terminal, and different user terminals may be connected to each other in a wired or wireless manner. User terminals can be widely applied to various scenarios. For example, near field communications (NFC) device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, etc.

[0170] The user terminal may also be referred to as a mobile station (MS), a terminal, a terminal device, and may further include a subscriber unit, a cellular phone, a smart phone, a wireless data card, a personal digital assistant (PDA) computer, a tablet computer, a wireless modem handheld device, a laptop computer, a cordless phone, or a wireless local loop (WLL) station, a machine type communication (MTC) terminal, etc. For the sake of convenience of description, in all embodiments of the present application, the devices mentioned above are collectively referred to as user terminals.

[0171] The foregoing user terminal may further include an antenna and a transceiver. The transceiver adjusts (for example: analog conversion, filtering, amplification, and upconversion, etc.) the output samples and generates an uplink signal, and the uplink signal is transmitted to the network device via the antenna; on the downlink, the antenna receives the downlink signal transmitted by the network device, and the transceiver adjusts (for example: filtering, amplification, downconversion, and digitization, etc.) the signal received from the antenna and provides input samples. The processor 1501 is used to execute the method for reconstructing beam feature parameters or the method for determining the beam described in the above embodiments. The embodiments of the present application do not limit the specific technologies and specific device forms adopted by the user terminal.

[0172] As another implementation, the above-mentioned electronic device 1500 can be a network device, and the user terminal can be connected to the network device wirelessly. The network device can also be connected to or communicate with the Evolved Universal Terrestrial Radio Access (E-UTRA) system, New Radio (NR) system, and future wireless access systems defined in the 3rd Generation Partnership Project (3GPP), or a WiFi system. The network device can also be connected to devices including those in two or more different wireless access systems mentioned above. The network device can also be connected to an Open Radio Access Network (O-RAN).

[0173] Modules for implementing base station functions may be configured on the network device. The modules for implementing base station functions can implement the functions of the following devices: Base Station, Evolved NodeB (eNodeB or eNB), Transmission Reception Point (TRP), Next Generation NodeB (gNB) in the 5th Generation (5G) mobile communication system, Next Generation NodeB in the 6th Generation (6G) mobile communication system, base stations in future mobile communication systems, or access nodes in a WiFi system.

[0174] The foregoing base station may further include an antenna and a transceiver. In the uplink, the uplink signal from the user terminal is received via the antenna, demodulated by the transceiver, and further processed by the processor 1501 to recover the signaling information sent by the user terminal; in the downlink, the signaling message is processed by the processor 1501, modulated by the transceiver to generate a downlink signal, and transmitted to the user terminal via the antenna. The processor 1501 is also used to execute the method for reconstructing beam feature parameters or the method for determining a beam described in the above embodiments. The base station may include a macro base station, a micro base station, or an indoor station, and may also be a relay node or a donor node.

[0175] It can be understood that the above only introduces a simplified design of the base station. In practical applications, the base station may include any number of transmitters, receivers, processors, controllers, memories, communication units, etc., and all base stations that can implement the present application are within the protection scope of the present application.

[0176] Among them, the processor 1501 includes one or more, which can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 1501 can be a general-purpose processor, including a central processing unit (CPU), a microcontroller unit (MCU), a network processor (NP), or other conventional processors; it can also be a dedicated processor, including a neural-network processing unit (NPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Moreover, when there are multiple processor 1501s, a part of them can be general-purpose processors and another part can be dedicated processors.

[0177] The memory 1503 includes one or more, which can be, but are not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0178] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for reconstructing beam characteristic parameters, characterized in that Applied to a user terminal, the method comprises: receiving a reference signal of each beam in a beam subset sent by a network device, wherein the beam subset includes a singular beam, and a correlation between the singular beam and other beams in the full beam set in which the beam subset is located is lower than a correlation threshold; Measuring a measurement beam characteristic parameter of each beam in the beam subset based on the reference signal; The predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set are reconstructed based on the measured beam characteristic parameters of each beam in the beam subset.

2. The method according to claim 1, wherein The method further comprises: receiving a reference signal of each beam in the entire beam set sent by the network device at different times; At any moment, measuring a measurement beam characteristic parameter of each beam in the entire beam set based on the reference signal sent at the moment; Determine the singular beam in the entire beam set according to the measurement beam characteristic parameter corresponding to at least one moment; Send beam information corresponding to the singular beam to the network device.

3. The method according to claim 2, wherein The determining the singular beam in the entire beam set according to the measurement beam characteristic parameter corresponding to at least one moment includes: For any beam in the full beam set at any moment, determine a correlation measurement parameter between the beam and the adjacent beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam; Whether the beam is the singular beam is determined according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment.

4. The method according to claim 3, wherein The determining the singular beam in the entire beam set according to the measurement beam characteristic parameter corresponding to at least one moment also includes: For any beam in the entire beam set at any moment, determine a significance parameter of the beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam; Correspondingly, determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment includes: Determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment and the significance parameter; or, The determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment includes: At any moment, determining a beam classification result of the beam according to the correlation metric parameter of the beam and the correlation metric parameters of other beams in the entire beam set; Determine whether the beam is the singular beam according to the beam classification result and the correlation measurement parameter of the beam.

5. A method for reconstructing beam characteristic parameters, characterized in that, Applied to a network device, the method comprises: Sending a reference signal of each beam in a beam subset to a user terminal, wherein the beam subset includes a singular beam, and a correlation between the singular beam and other beams in the full beam set in which the beam subset is located is lower than a correlation threshold; Receiving a measured beam characteristic parameter of each beam in the beam subset sent by the user terminal; The predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set are reconstructed based on the measured beam characteristic parameters of each beam in the beam subset.

6. The method according to claim 5, characterized in that, Before sending the reference signal of each beam in the beam subset to the user terminal, the method further includes: The beam subset is updated based on beam information corresponding to the singular beam.

7. The method according to claim 5 or 6, characterized in that, The method further comprises: Sending, at different times, a reference signal of each beam in the entire beam set to the user terminal; At any moment, receiving a measured beam characteristic parameter of each beam in the entire beam set obtained by the user terminal based on the reference signal sent at the moment; The singular beam in the entire beam set is determined according to the measurement beam characteristic parameter corresponding to at least one moment.

8. A method for determining a beam, characterized in that include: Obtaining a measured beam characteristic parameter obtained by measuring a reference signal of each beam in a full beam set sent by a user terminal at different times based on a network device; A singular beam in the beam set is determined according to the measurement beam characteristic parameter corresponding to at least one moment, wherein a correlation between the singular beam and other beams in the beam set is lower than a correlation threshold.

9. A method for reconstructing object characteristic parameters, characterized in that include: Acquire a measurement object characteristic parameter of each object in the object subset, wherein the object subset includes a singular object, and the correlation between the singular object and other objects in the full object set where the object subset is located is lower than a correlation threshold; The predicted object feature parameters of other objects in the entire object set except the object subset are reconstructed based on the measured object feature parameters of each object in the object subset.

10. An electronic device, characterized in that, include: processor, memory, and bus; The processor and the memory communicate with each other via the bus; The memory stores computer program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 9 by calling the computer program instructions.

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