Methods for reconstructing beam characteristic parameters, methods for determining beams, methods and equipment for reconstructing object characteristic parameters.

Abstract

This application provides a method for reconstructing beam characteristic parameters, a method for determining beams, a method and apparatus for reconstructing object characteristic parameters, relating to the field of data reconstruction technology. The method for reconstructing beam characteristic parameters applied to a user terminal includes: receiving reference signals of each beam in a beam subset sent by a network device, wherein the beam subset includes singular beams, and the correlation between the singular beams and other beams in the full beam set containing the beam subset is below a correlation threshold; measuring the measured beam characteristic parameters of each beam in the beam subset based on the reference signals; and reconstructing the predicted beam characteristic parameters of other beams in the full beam set excluding the beam subset based on the measured beam characteristic parameters of each beam in the beam subset. The above method can obtain beam characteristic parameters of beams with high correlation through reconstruction, or beam characteristic parameters of beams with low correlation through measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

Description

Technical Field

[0001] This application relates to the field of data reconstruction technology, and more specifically, to a method for reconstructing beam characteristic parameters, a method for determining beams, a method for reconstructing object characteristic parameters, and an electronic device. Background Technology

[0002] Reconstruction is a task or technique that aims to restore the complete form of original data using a model; that is, to reconstruct an estimated version of the original or target data based on partial or distorted information. In other words, it involves having the model guess the missing parts or reconstruct the obscured data. However, in the process of reconstructing missing or obscured data from partial information, some predictions are inaccurate, resulting in lower accuracy of the reconstructed predictions.

[0003] Taking beam reconfiguration as an example, spatial domain downlink beam reconfiguration can be performed on the entire beam set based on the measurement results of beam subsets, thereby reducing signaling overhead and prediction latency. However, currently, when reconstructing the prediction results of the entire beam set using the measurement results of beam subsets, the prediction results of some beams are inaccurate, resulting in low accuracy of the prediction results of the reconstructed entire beam set. Summary of the Invention

[0004] The purpose of this application is to provide a method for reconstructing beam characteristic parameters, a method for determining beams, a method for reconstructing object characteristic parameters, and an electronic device, in order to solve the technical problem in the prior art that the accuracy of the predicted results of the reconstructed full beam set is low when reconstructing the prediction results of the full beam set through the measurement results of a subset of beams.

[0005] In a first aspect, embodiments of this application provide a method for reconstructing beam characteristic parameters, applied to a user terminal. The method includes: receiving reference signals of each beam in a beam subset sent by a network device, wherein the beam subset includes singular beams, and the correlation between the singular beams and other beams in the full beam set to which the beam subset is located is lower than a correlation threshold; measuring measured beam characteristic parameters of each beam in the beam subset based on the reference signals; and reconstructing predicted beam characteristic parameters of other beams in the full beam set excluding the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0006] In the above scheme, during the process of reconstructing the predicted beam characteristic parameters of other beams in the full beam set (excluding the beam subset) using the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation to other beams in the full beam set can be added to the beam subset. This allows the beam characteristic parameters of the singular beams to be obtained through measurement even if they cannot be obtained through reconstruction. Therefore, the method provided in this application can obtain the beam characteristic parameters of highly correlated beams through reconstruction, and also obtain the beam characteristic parameters of less correlated beams through measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

[0007] In an optional implementation, the method further includes: receiving reference signals of each beam in the full beam set transmitted by the network device at different times; for any given time, measuring the measured beam characteristic parameters of each beam in the full beam set based on the reference signals transmitted at that time; determining the singular beam in the full beam set based on 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 full beam set through the measured beam characteristic parameters of a subset of beams, the correlation between the beam and other beams in the full beam set can be determined based on the measured beam characteristic parameters of the beam, thereby identifying the singular beam with higher accuracy.

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

[0009] In an optional implementation, determining the singular beam in the full beam set based on the measured beam characteristic parameters corresponding to at least one time further includes: for any beam in the full beam set at any time, determining the saliency parameter of the beam based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beam corresponding to the beam; correspondingly, determining whether the beam is the singular beam based on the correlation metric parameter between the beam and the adjacent beam at at least one time includes: determining whether the beam is the singular beam based on the correlation metric parameter between the beam and the adjacent beam at at least one time and the saliency parameter. In the above scheme, the saliency parameter can be used to test whether the local numerical spatial autocorrelation is non-randomly significant. Therefore, by determining the saliency parameter of the beam, the coincidence of the singular beam determined based on the correlation metric parameter can be reduced, thereby improving the accuracy of the determined singular beam.

[0010] Secondly, embodiments of this application provide a method for reconstructing beam characteristic parameters, applied to a network device. The method includes: sending reference signals of each beam in a beam subset to a user terminal, wherein the beam subset includes singular beams, and the correlation between the singular beams and other beams in the full beam set to which the beam subset is located is lower than a correlation threshold; receiving measured beam characteristic parameters of each beam in the beam subset sent by the user terminal; and reconstructing predicted beam characteristic parameters of other beams in the full beam set excluding the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

[0011] In the above scheme, during the process of reconstructing the predicted beam characteristic parameters of other beams in the full beam set (excluding the beam subset) using the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation to other beams in the full beam set can be added to the beam subset. This allows the beam characteristic parameters of the singular beams to be obtained through measurement even if they cannot be obtained through reconstruction. Therefore, the method provided in this application can obtain the beam characteristic parameters of highly correlated beams through reconstruction, and also obtain the beam characteristic parameters of less correlated beams through measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

[0012] In an optional implementation, 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 beams. In this scheme, after receiving the beam information of the singular beams sent by the user terminal, the network device may not add all the singular beams to the beam subset, but instead update the beam subset based on the beam information of the singular beams, thereby improving the flexibility of updating the beam subset.

[0013] In an optional implementation, the method further includes: sending reference signals of each beam in the full beam set to the user terminal at different times; for any given time, receiving measured beam characteristic parameters of each beam in the full beam set obtained by the user terminal based on the reference signals sent at that time; and determining the singular beam in the full beam set based on 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 full beam set through the measured beam characteristic parameters of a subset of beams, the correlation between the beam and other beams in the full beam set can be determined based on the measured beam characteristic parameters of the beam, thereby determining the singular beam with higher accuracy.

[0014] Thirdly, embodiments of this application provide a method for determining a beam, comprising: acquiring measured beam characteristic parameters obtained by a user terminal based on reference signals of each beam in a full beam set transmitted by a network device at different times; determining singular beams in the full beam set according to the measured beam characteristic parameters corresponding to at least one time, wherein the correlation between the singular beams and other beams in the full 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 full beam set, singular beams with low correlation to other beams in the full beam set can be identified. Thus, the identified singular beams 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, thereby improving the accuracy of the prediction results of the full beam set obtained by reconstruction.

[0016] Fourthly, embodiments of this application provide 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 singular objects, and the correlation between the singular objects and other objects in the full set of objects in which the object subset is located is lower than a correlation threshold; and reconstructing predicted object feature parameters of other objects in the full set of objects excluding the object subset based on the measured object feature parameters of each object in the object subset.

[0017] In the above scheme, during the process of reconstructing the predicted object feature parameters of other objects in the entire object set (excluding the object subset) using the measured object feature parameters of each object in the object subset, singular objects with low correlation to other objects in the entire object set can be added to the object subset. This allows the object feature parameters to be obtained through measurement even if the object feature parameters corresponding to the singular objects cannot be obtained through reconstruction. Therefore, the method provided in this application embodiment can obtain the object feature parameters of both highly correlated and low-correlation objects through reconstruction, thereby improving the accuracy of the prediction results of the reconstructed entire object set.

[0018] Fifthly, embodiments of this application provide an electronic device, including: a processor, a memory, and a 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 methods described in the first, second, third, or fourth aspects by calling the computer program instructions.

[0019] Specifically, if the electronic device is a user terminal, the electronic device performs the method described in the first, third, or fourth aspect; if the electronic device is a network-side device, the electronic device performs the method described in the second, third, or fourth aspect. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. The following drawings only show some embodiments of this application and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings.

[0021] Figure 1 A flowchart illustrating a beam determination method provided in an embodiment of this application;

[0022] Figure 2 A schematic diagram of an observation matrix provided for an embodiment of this application;

[0023] Figure 3 A flowchart illustrating a method for reconstructing beam characteristic parameters applied to a user terminal, provided in an embodiment of this application;

[0024] Figure 4 An interactive diagram illustrating the first beam determination method provided in this application embodiment;

[0025] Figure 5 A flowchart illustrating a method for reconstructing beam characteristic parameters in a network device, provided in an embodiment of this application;

[0026] Figure 6 An interactive diagram illustrating the second beam determination method provided in this application embodiment;

[0027] Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] When reconstructing the original or target data of the entire object set using partial information from a subset of objects, inaccurate predictions for some objects can lead to lower accuracy in the reconstructed object predictions. Therefore, singular objects with low correlation to other objects in the entire object set can be added to the subset to improve the accuracy of the reconstructed object predictions. In view of this, this application provides a method for reconstructing object feature parameters, which may include: Step 1: Obtaining the measured object feature parameters of each object in the subset; Step 2: Reconstructing the predicted object feature parameters of other objects in the entire object set, excluding the subset, based on the measured object feature parameters of each object in the subset.

[0029] Specifically, the object subset includes singular objects. The correlation between singular objects and other objects in the full set of objects to which the object subset belongs is lower than the correlation threshold. Therefore, in the process of reconstructing the predicted object feature parameters of other objects in the full set of objects excluding the object subset based on the measured object feature parameters of each object in the object subset, the object feature parameters of objects with high correlation can be obtained through reconstruction, and the object feature parameters of objects with low correlation can be directly obtained.

[0030] Prior to step 1 above, a step of identifying singular objects in the entire set of objects can be performed. It should be noted that this application does not specifically limit the implementation method for identifying 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, it can be determined whether an object is a singular object by calculating the correlation measurement parameters of each object in the entire set of objects; or, it can be determined whether an object is a singular object by calculating the correlation measurement parameters of each object in the entire set of objects and then determining the salience parameters corresponding to the object, etc.

[0031] Furthermore, the method of this application embodiment can be applied in a variety of scenarios, such as: the object can be a beam, user terminal, sensor, image pixel, etc.

[0032] Taking beams 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, sending the reference signals of all beams to the user terminal consumes significant resources. Therefore, a reconstruction method can be used, where the network device only needs to send the reference signals corresponding to a subset of beams 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 (e.g., RSRP) of each beam in the beam subset based on the reference signals. Then, based on the measured beam characteristic parameters of each beam in the beam subset, the predicted beam characteristic parameters of the other beams in the entire beam set, excluding the beam subset, can be reconstructed.

[0033] Taking user terminals as an example, in wireless communication deployment and optimization, operators want to understand the signal propagation of network devices in space (i.e., wireless coverage map). However, the measurement points of user terminals are sparse and uncontrollable, and the channel information of continuous geographical areas cannot be fully observed. Therefore, a reconstruction method can be adopted. The network device only needs to obtain the measurement terminal characteristic parameters of each terminal in the terminal subset (e.g., user terminal location, signal-to-noise ratio (SNR), etc.), without needing to obtain the measurement terminal characteristic parameters of each terminal in the entire terminal set. Thus, the spatial signal field can be reconstructed based on the information reported by a small number of terminals. Specifically, the network device can obtain the measurement terminal characteristic parameters of each terminal in the terminal subset, and then reconstruct the predicted terminal characteristic parameters of other terminals in the entire terminal set, excluding the terminal subset, based on the measurement terminal characteristic parameters of each terminal in the terminal subset.

[0034] Taking sensors as an example, in atmospheric environmental monitoring and urban sensing systems, sensors (e.g., gas sensors, temperature sensors, humidity sensors, etc.) are deployed at several fixed locations in the city. In cases of sparse deployment, the vast majority of areas in the city remain unobserved. Therefore, a reconstruction method can be used, requiring only the measured sensor characteristic parameters (e.g., sensor observation values) of each sensor in a subset of deployed sensors, without needing to obtain the measured sensor parameters of all sensors in the entire sensor set. This allows for the prediction of environmental values ​​at other spatial locations based on a small number of known observation points, resulting in a complete pollution heat map or temperature map. Specifically, electronic devices can acquire the measured sensor characteristic parameters of each sensor in the subset, and then reconstruct the predicted sensor characteristic parameters of all sensors in the entire sensor set, excluding the subset, based on these parameters.

[0035] In the above scheme, during the process of reconstructing the predicted object feature parameters of other objects in the entire object set (excluding the object subset) using the measured object feature parameters of each object in the object subset, singular objects with low correlation to other objects in the entire object set can be added to the object subset. This allows the object feature parameters to be obtained through measurement even if the object feature parameters corresponding to the singular objects cannot be obtained through reconstruction. Therefore, the method provided in this application embodiment can obtain the object feature parameters of both highly correlated and low-correlation objects through reconstruction, thereby improving the accuracy of the prediction results of the reconstructed entire object set.

[0036] The following describes in detail the specific implementation of the above-described method for reconstructing object feature parameters, using the object as a beam as an example. It is understood that the specific implementation described below can be applied to other objects, and those skilled in the art can make corresponding substitutions; this application's embodiments will not elaborate further on these substitutions. For example, the beam set can be replaced with another object set, or the beam feature parameters of the beam can be replaced with the object feature parameters corresponding to other objects, etc.

[0037] Before introducing the solution provided in this embodiment, the training process of the reconstruction model in beam management will be explained. Using the trained reconstruction model, spatial domain downlink beam prediction can be performed on beam set A based on the measurement results of beam set B (Set B).

[0038] Specifically, Set B is a smaller set 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 (SSBs), while Set A can be a set of CSI-RS. For instance, Set B includes CSI-RS#[2,4,6,8] (i.e., the CSI-RS corresponding to beams with beam identifiers of 2, 4, 6, and 8), and Set A includes CSI-RS#[1,2,3,4,5,6,7,8] (i.e., the CSI-RS corresponding to beams with beam identifiers of 1, 2, 3, 4, 5, 6, 7, and 8).

[0039] The base station selects Set B from Set A and sends Set B to the user terminal. The terminal measures Set B to obtain its RSRP (Responsible RSRP), and uses this RSRP measurement as input or part of the input to a reconstruction model. The reconstruction model is then used to predict the RSRP of Set A. Repeating this training process yields a trained reconstruction model. Using this trained model, the RSRP of Set A can be reconstructed based on the RSRP of Set B. Without this reconstruction model, the base station would need to send the entire Set A to the terminal, increasing resource overhead. However, with this reconstruction model, the base station only needs to send Set B, i.e., a part of Set A, to the terminal, reducing resource overhead.

[0040] 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. After the terminal measures Set B, it obtains RSRP {#1, 3, 5, 7}, which is then used as input to the reconstruction model. The output is the predicted value of RSRP of the 8 reference signals in Set A, or the probability that each reference signal in Set A has the largest RSRP measurement value in Set A, i.e., the prediction probability.

[0041] 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 to other beams, in the process of reconstructing the RSRP of Set A using the RSRP of Set B, it may be impossible to reconstruct the RSRP of these beams, resulting in low accuracy of the reconstructed RSRP of Set A.

[0042] In view of this, embodiments of this application provide a method for reconstructing beam characteristic parameters and a method for determining beams. The method for reconstructing beam characteristic parameters is used to reconstruct the predicted beam characteristic parameters of each beam in the full beam set based on the measured beam characteristic parameters of each beam in a beam subset. Unlike beam reconstruction processes in the prior art, the beam subset in the method for reconstructing beam characteristic parameters provided in this application includes singular beams (i.e., beams with low correlation to other beams); the method for determining beams is used to determine these singular beams.

[0043] It is understandable that the singular beams in the method for reconstructing beam characteristic parameters can be determined using the beam determination method; and the singular beams determined by the beam determination method can be used in the method for reconstructing beam characteristic parameters, or for other purposes.

[0044] It should be noted that the above method for reconstructing beam characteristic parameters can be executed by either the user terminal or the network device. If the method for reconstructing beam characteristic parameters is executed by the user terminal, the reconstruction model is deployed on the user terminal; 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 by either the user terminal or the network device.

[0045] For example, in the first implementation, both the method for reconstructing beam characteristic parameters and the method for determining the beam are executed by the user terminal; in the second implementation, both the method for reconstructing beam characteristic parameters and the method for determining the beam are executed by the network device; in the third implementation, the method for reconstructing beam characteristic parameters is executed by the user terminal, and the method for determining the beam is executed by the network device; in the fourth implementation, 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 terminal.

[0046] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0047] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a beam determination method provided in an embodiment of this application. This beam determination method can be executed by either a user terminal or a network device. The beam determination method may include:

[0048] S101: Obtain the measurement beam characteristic parameters obtained by measuring the reference signals of each beam in the full beam set transmitted by the user terminal at different times based on the network device. S102: Determine the singular beams in the full beam set based on the measurement beam characteristic parameters corresponding to at least one time moment.

[0049] The network device stores the beam identifiers of each beam in the beam set. The network device can send reference signals of each beam in the beam set to the user terminal. After receiving the reference signals of each beam in the beam set sent by the network device, the user terminal can obtain the measurement beam characteristic parameters of each beam in the beam set based on the reference signals.

[0050] This application does not specifically limit the form of the reference signal and the measured beam characteristic parameters. The measured beam characteristic parameters are used to screen the optimal beam, so any parameter that can reflect whether the beam is optimal is acceptable, such as RSRP, beam angle of arrival, beamwidth, etc. The reference signal is used to measure the above-mentioned measured beam characteristic parameters, so different reference signals can be used depending on the measured beam characteristic parameters. Those skilled in the art can make appropriate adjustments according to the actual situation. For example, the reference signal may include SSB, CSI-RS, etc.

[0051] Optionally, based on the reference signals of each beam in the full beam set transmitted by the network device at different times, the terminal can measure different beam characteristic parameters. These different times can include the time when the user terminal switches access cells, the time when the communication environment changes, the time when the user terminal's location changes, etc. Therefore, the aforementioned full beam set can correspond to multiple sets of beam characteristic parameters, each set including the beam characteristic parameters measured by the terminal based on the reference signals of each beam in the full beam set transmitted by the network device at a certain time.

[0052] If the beam determination method provided in this application embodiment is executed by a user terminal, the terminal receives reference signals of each beam in the full beam set sent by the network device at different times, and measures the measured beam characteristic parameters of each beam in the full beam set based on the reference signals sent at any given time, thereby obtaining the measured beam characteristic parameters in S101.

[0053] If the beam determination method provided in this application embodiment is executed by a network device, the network device sends reference signals of each beam in the beam set to the user terminal at different times, and receives the measured beam characteristic parameters of each beam in the beam set obtained by the user terminal based on the reference signals sent at that time at any given time, thereby obtaining the measured beam characteristic parameters in S101.

[0054] In S102, a singular beam refers to a beam whose correlation with other beams in the beam set is lower than a correlation threshold. In this embodiment, the form and value of the correlation threshold are not specifically limited; it can be a single value or a set of multiple values, and its magnitude can be adjusted according to actual circumstances.

[0055] Regarding S102, as one implementation method, singular beams in the beam set can be determined based on the measurement beam characteristic parameters corresponding to a certain time. For example, the correlation metric parameter corresponding to the beam can be determined based on the measurement beam characteristic parameters corresponding to a certain time, and then it can be determined whether the beam is a singular beam; or, the correlation metric parameter of the beam can be determined based on the measurement beam parameters corresponding to a certain time, and then the saliency parameter corresponding to the beam can be determined, and then it can be determined whether the beam is a singular beam, etc.

[0056] Regarding S102, as another implementation, singular beams in the beam set can be determined based on the measurement beam characteristic parameters corresponding to multiple times. For example, correlation measurement parameters corresponding to beams can be determined based on the measurement beam characteristic parameters corresponding to multiple times, and the correlation measurement parameters can be statistically analyzed to determine whether the beam is a singular beam; or, correlation measurement parameters corresponding to beams can be determined based on the measurement beam characteristic parameters corresponding to multiple times, and then the significance parameters corresponding to beams can be determined, and the significance parameters can be statistically analyzed to determine whether the beam is a singular beam, etc.

[0057] In the above scheme, based on the measured beam characteristic parameters of each beam in the full beam set, singular beams with low correlation to other beams in the full beam set can be identified. Thus, the identified singular beams 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, thereby improving the accuracy of the prediction results of the full beam set obtained by reconstruction.

[0058] The following describes four specific implementation methods for determining singular beams. The first method uses correlation measurement parameters to determine singular beams; the second method uses both correlation measurement parameters and significance parameters to determine singular beams; the third method uses both correlation measurement parameters and beam classification results to determine singular beams; and the fourth method uses both correlation measurement parameters, significance parameters, and beam classification results to determine singular beams.

[0059] The following describes a specific implementation method for determining singular beams, namely, using correlation measurement parameters to determine singular beams. In this case, S102 may include: S201: For any beam in the beam set at any given time, determine the correlation measurement parameters between the beam and its adjacent beams based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams corresponding to the beam. S202: Determine whether the beam is a singular beam based on the correlation measurement parameters between the beam and its adjacent beams at at least one time.

[0060] In S201, the adjacent beam corresponding to a beam refers to the beam in the beam set that is smaller in distance from the beam itself. This application embodiment does not specifically limit the meaning of the distance between two beams in the beam set; for example, the distance can characterize the distance between two beams in the beam matrix, etc. The beam matrix includes multiple beams arranged in a certain order.

[0061] For different distance representation methods, the embodiments of this application do not specifically limit the specific implementation method for determining the adjacent beams of a beam. For example, the physical distance between two beams can be calculated using the beam arrival angle and the adjacent beams can be found, or the adjacent beams can be found from the beam matrix using a sliding window, etc.

[0062] The correlation metric between a beam and its neighboring beams is used to characterize the correlation between a beam and its neighboring beams. This application does not specifically limit the form of the correlation metric; for example, the correlation metric can be one of the following: the local Moran index of the beam, the correlation coefficient between beams, or the full beam correlation matrix.

[0063] For different correlation measurement parameters, the embodiments of this application do not impose specific limitations on the specific implementation method for 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.

[0064] It should be noted that S201 can be executed once for each beam in the full beam set at a given time, and S201 can be executed multiple times for each time (the number of executions can be the same as the number of beams in the full beam set).

[0065] In S202, as one implementation, singular beams in the beam set can be determined based on the correlation metric parameter corresponding to a single time moment. For example, the correlation metric parameter corresponding to a beam can be determined based on the measurement beam characteristic parameter corresponding to a single time moment, thereby determining whether the beam is a singular beam. As another implementation, singular beams in the beam set can be determined based on the correlation metric parameters corresponding to multiple time moments. For example, the correlation metric parameter corresponding to a beam can be determined based on the measurement beam characteristic parameters corresponding to multiple time moments, and the correlation metric parameters can be statistically analyzed to determine whether the beam is a singular beam.

[0066] The following example illustrates a specific implementation of S202. Whether a beam is a singular beam can be determined by judging whether it satisfies any of the following conditions: If the beam satisfies any of the following conditions, it is characterized as a singular beam:

[0067] Condition 2: The correlation measurement parameter is less than the first threshold, where the correlation threshold is the first threshold.

[0068] Specifically, if the correlation metric parameter is less than the first threshold, the beam is considered to have low correlation with adjacent beams, and therefore can be identified as a singular beam. The specific value of the first threshold can be adjusted according to the actual situation; for example, when the correlation metric parameter is a local Moran parameter, the first threshold can be 0.

[0069] It should be noted that when determining singular beams in the beam set based on the correlation metric parameters corresponding to a single moment, if a beam in the beam set at that moment satisfies condition two above, then that beam can be identified as a singular beam; when determining singular beams in the beam set based on the correlation metric parameters corresponding to multiple moments, if a beam in the beam set at any moment satisfies condition two above, then that beam can be identified as a singular beam.

[0070] Condition 9: The number of times the beam is identified as the first candidate singular beam based on multiple measured beam characteristic parameters according to Condition 2 is greater than the second threshold, wherein the correlation threshold includes the second threshold.

[0071] Specifically, a first candidate singular beam refers to a beam that, based on the measured beam characteristic parameters at a certain moment, can be identified as a singular beam according to condition two. In other words, for any given moment, the correlation metric parameter of the beam can be determined based on the corresponding measured beam characteristic parameters. Then, according to condition two and the aforementioned correlation metric parameter, it can be determined whether the beam is a singular beam at that moment. If the beam is a singular beam at that moment, it is identified as a first candidate beam.

[0072] By statistically analyzing the number of times the measured beam characteristic parameters at multiple time points are identified as the first candidate singular beam according to condition two, and considering that the number of times this number is greater than a second threshold, it can be assumed that the beam has a low correlation with adjacent beams, and therefore the beam can be identified as a singular beam. The specific value of the second threshold can be adjusted according to the actual situation.

[0073] In the above scheme, the correlation measurement parameter can be used to characterize the correlation between two objects. Therefore, by determining the correlation measurement parameter between a beam and its neighboring beams, the singular beams with low correlation with other beams in the beam set can be identified more accurately.

[0074] The following describes a second specific implementation method for determining singular beams, namely, determining singular beams using correlation measurement parameters and significance parameters. In this case, S102 may include:

[0075] S301: For any beam in the full beam set at any given time, determine the correlation metric parameter between the beam and its neighboring beams based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams. S302: For any beam in the full beam set at any given time, determine the saliency parameter of the beam based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams. S303: Determine whether the beam is a singular beam based on the correlation metric parameter and saliency parameter between the beam and its neighboring beams at at least one time. The specific implementation of S301 is the same as that of S201, and will not be repeated here.

[0076] Optionally, as one 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 parameters in S301. In this case, S301 is executed first, followed by S302.

[0077] In S302, the significance parameter of the beam is used to test whether the local numerical spatial autocorrelation is non-randomly significant. This application does not specifically limit the specific implementation method for determining the significance parameter; for example, it may determine the significance parameter based on the significance test of the correlation coefficient, or based on a statistical measure, etc.

[0078] It should be noted that S302 can be executed once for each beam in the full beam set at a given time, and S302 can be executed multiple times for each time (the number of executions can be the same as the number of beams in the full beam set).

[0079] In S303, as one implementation, singular beams in the beam set can be determined based on the correlation measurement parameter and significance parameter corresponding to a single time moment. For example, the correlation measurement parameter and significance parameter corresponding to a beam can be determined based on the measurement beam characteristic parameter corresponding to a single time moment, thereby determining whether the beam is a singular beam. As another implementation, singular beams in the beam set can be determined based on the correlation measurement parameter and significance parameter corresponding to multiple time moments. For example, the correlation measurement parameter and significance parameter corresponding to a beam can be determined based on the measurement beam characteristic parameter corresponding to multiple time moments, and the correlation measurement parameter and significance parameter can be statistically analyzed to determine whether the beam is a singular beam.

[0080] The following example illustrates a specific implementation of S303. Whether a beam is a singular beam can be determined by judging whether it satisfies any of the following conditions: If the beam satisfies any of the following conditions, it indicates that the beam is a singular beam:

[0081] Condition 6: The correlation measurement parameter is less than the first threshold and the significance parameter is less than the fourth threshold, where the correlation threshold includes the first threshold and the fourth threshold.

[0082] Specifically, a correlation parameter less than the first threshold indicates a low correlation between the beam and its neighboring beams, while a significance parameter less than the fourth threshold suggests a high degree of confidence in the low correlation between the beam and its neighboring beams, thus classifying the beam as a singular beam. The specific values ​​of the first and third thresholds can be adjusted according to actual conditions, and there is no correlation between their values.

[0083] Optionally, when determining singular beams in the beam set based on the correlation measurement parameter and significance parameter corresponding to a certain time, if a certain beam in the beam set at that time satisfies condition six above, then that beam can be determined as a singular beam; when determining singular beams in the beam set based on the correlation measurement parameter and significance parameter corresponding to multiple times, if a certain beam in the beam set at any time satisfies condition six above, then that beam can be determined as a singular beam.

[0084] Condition 10: The number of times the beam is identified as the first candidate singular beam based on multiple measured beam characteristic parameters according to Condition 6 is greater than the fifth threshold, wherein the correlation threshold includes the fifth threshold.

[0085] Specifically, a first candidate singular beam refers to a beam that, based on the measured beam characteristic parameters at a certain moment, can be identified as a singular beam according to condition six. In other words, for any given moment, the correlation metric and significance parameters of the beam can be determined based on the corresponding measured beam characteristic parameters. Then, according to condition six, the correlation metric, and the significance parameters, it can be determined whether the beam is a singular beam at that moment. If the beam is a singular beam at that moment, it is identified as a first candidate beam.

[0086] The number of times the measured beam characteristic parameters at multiple time points are identified as the first candidate singular beam according to condition six is ​​counted. If this number exceeds the fifth threshold, the beam is considered to have low correlation with adjacent beams, and therefore can be identified as a singular beam. The specific value of the fifth threshold can be adjusted according to actual conditions.

[0087] In the above scheme, 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 measurement parameter can be reduced, thereby improving the accuracy of the determined singular beam.

[0088] Then, the third specific implementation method for determining singular beams is introduced, namely, determining singular beams using correlation measurement parameters and beam classification results. In this case, S102 may include: S401, for any beam in the beam set at any given time, determining the correlation measurement parameters between the beam and its neighboring beams based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams. S402, for any given time, determining the beam classification result of the beam based on the correlation measurement parameters of the beam and the correlation measurement parameters of other beams in the beam set. S403, determining whether the beam is a singular beam based on the beam classification result and the correlation measurement parameters of the beam.

[0089] The specific implementation of S401 is the same as that of S201, and will not be repeated here. In S402, the embodiments of this application do not specifically limit the classification category corresponding to the beam classification result. For example, the beam classification result may include singular beams and non-singular beams, or it may include severely singular beams, moderately singular beams, and slightly singular beams, etc.

[0090] Furthermore, this application does not specifically limit the specific implementation method for determining the above-mentioned beam classification results. Optionally, the mean and variance can be calculated based on the correlation measurement parameters, and the beam classification results can be determined by comparing the relationship between the beam correlation measurement parameters and the above-mentioned mean and variance; or, the distribution of the correlation measurement parameters can be determined, and the beam classification results can be determined based on the beam correlation measurement parameters and the above-mentioned distribution, etc.

[0091] It should be noted that S402 can be executed once at each time point.

[0092] The following example illustrates a specific implementation of S403. Whether a beam is a singular beam can be determined by judging whether it satisfies any of the following conditions: If the beam satisfies any of the following conditions, it is characterized as a singular beam:

[0093] Condition 1: The beam classification result indicates that the beam is a singular beam.

[0094] Condition 2: For any given time, the correlation metric parameter is less than the first threshold, where the correlation threshold is the first threshold.

[0095] It should be noted that when determining singular beams in the full beam set based on the correlation measurement parameters and beam classification results corresponding to a certain time, if a beam in the full beam set at that time satisfies the above condition two, then that beam can be determined as a singular beam; when determining singular beams in the full beam set based on the correlation measurement parameters and beam classification results corresponding to multiple time points, if a beam in the full beam set at any time satisfies the above condition two, then that beam can be determined as a singular beam.

[0096] Condition 3: The number of times the beam is identified as the first candidate singular beam according to Condition 1 or Condition 2 at multiple times is greater than the second threshold, wherein the correlation threshold includes the second threshold.

[0097] Condition 4: The sum of the number of times the beam is 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 3 is greater than the third threshold, wherein the correlation threshold includes the third threshold.

[0098] Specifically, a second candidate singular beam refers to a beam that can be identified as a first candidate beam according to either condition one or condition two. In other words, for any given moment, it can be determined whether the beam is a singular beam at that moment based on the corresponding measured beam characteristic parameters. If the beam is a singular beam at that moment, it is identified as a second candidate beam.

[0099] The number of times the measured beam characteristic parameters at multiple time points are identified as the third candidate singular beam according to condition three is counted. Furthermore, based on the beam classification results corresponding to the beam, a corresponding number of counts can be assigned to the beam. If the sum of these two counts is greater than a third threshold, the beam is considered to have low correlation with adjacent beams, and therefore can be identified as a singular beam. The specific value of the third threshold can be adjusted according to actual circumstances.

[0100] In the above scheme, after determining the first candidate singular beam based on the correlation metric parameter for any given time, the randomness can be reduced by statistically analyzing the number of times the first candidate singular beam is determined across multiple time points, thereby improving the accuracy of the determined singular beam. Furthermore, by classifying the singular beams, the method for determining the singular beam can be further optimized according to user needs, thereby improving the adaptability of the determined singular beam.

[0101] The following describes the fourth specific implementation method for determining singular beams, which utilizes correlation measurement parameters, significance parameters, and beam classification results to determine singular beams. In this case, S102 may include:

[0102] S501. For any beam in the full beam set at any given time, determine the correlation metric parameter between the beam and its neighboring beams based on the beam characteristic parameters measured for that beam and the measured beam characteristic parameters of its adjacent beams. S502. For any beam in the full beam set at any given time, determine the saliency parameter of the beam based on the beam characteristic parameters measured for that beam and the measured beam characteristic parameters of its adjacent beams. S503. For any given time, determine the beam classification result of the beam based on the correlation metric parameter of the beam and the correlation metric parameters of other beams in the full beam set. S504. Determine whether the beam is a singular beam based on the beam classification result, the correlation metric parameter, and the saliency parameter.

[0103] 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. They will not be described again here.

[0104] The following example illustrates a specific implementation of S504. Whether a beam is a singular beam can be determined by judging whether it satisfies any of the following conditions: If the beam satisfies any of the following conditions, it is characterized as a singular beam:

[0105] Condition 5: The beam classification result indicates that the beam is a singular beam.

[0106] Condition 6: For any given time, the correlation metric parameter is less than the first threshold and the significance parameter is less than the fourth threshold, where the correlation threshold includes both the first and fourth thresholds.

[0107] It should be noted that when determining singular beams in the beam set based on the correlation measurement parameters, significance parameters, and beam classification results corresponding to a single moment, if a beam in the beam set at that moment satisfies condition six above, then that beam can be identified as a singular beam. When determining singular beams in the beam set based on the correlation measurement parameters, significance parameters, and beam classification results corresponding to multiple moments, if a beam in the beam set at any moment satisfies condition six above, then that beam can be identified as a singular beam.

[0108] Condition 7: The number of times the beam is identified as the first candidate singular beam according to Condition 5 or Condition 6 at multiple times is greater than the fifth threshold, wherein the correlation threshold includes the fifth threshold.

[0109] Condition 8: The sum of the number of times the beam is 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 7 is greater than the sixth threshold, wherein the correlation threshold includes the sixth threshold.

[0110] Specifically, a second candidate singular beam refers to a beam that can be identified as a first candidate beam according to condition five or condition six. In other words, for any given moment, it can be determined whether the beam is a singular beam at that moment based on the corresponding measured beam characteristic parameters. If the beam is a singular beam at that moment, it is identified as a second candidate beam.

[0111] The number of times the measured beam characteristic parameters at multiple time points are identified as the third candidate singular beam according to condition seven is counted. Furthermore, based on the beam classification results corresponding to the beam, a corresponding number of counts can be assigned to the beam. If the sum of these two counts is greater than a third threshold, the beam is considered to have low correlation with adjacent beams, and therefore can be identified as a singular beam. The specific value of the third threshold can be adjusted according to actual circumstances.

[0112] In the above scheme, after determining the first candidate singular beam based on the correlation and significance parameters for any given time, the accuracy of the determined singular beam can be improved by statistically analyzing the number of times the first candidate singular beam is determined across multiple time points, thus reducing randomness. Furthermore, by classifying the singular beams, the method for determining the singular beams can be further optimized according to user needs, thereby improving the adaptability of the determined singular beams.

[0113] Furthermore, based on the above embodiments, the correlation measurement parameter can be the local Moran index.

[0114] In the above scheme, the local Moran index is an important indicator used in spatial statistical analysis to measure the local spatial correlation between a spatial cell and its neighbors. The local space is the matrix space in the observation matrix. By determining the local Moran index corresponding to the beam in the matrix space, a correlation measurement parameter with high accuracy can be obtained.

[0115] The following describes the specific implementation of determining the significance parameter in the above embodiments, using the local Moran index as the correlation metric parameter. Specifically, S302 or S502 may include: S601: Iterative execution: Randomly select one beam from the beam and its adjacent beams as the center beam, and the other beams as adjacent beams of the center beam, and calculate the new local Moran index corresponding to the center beam; until the set number of iterations is reached. S602: Determine the significance parameter based on the local Moran index of the beam, the number of iterations, and the new local Moran index.

[0116] In S601, the specific implementation method for determining the adjacent beams of the center beam is the same as the specific implementation method for determining the adjacent beams of any beam in the above embodiments. In addition, the specific implementation method for calculating the new local Moran index corresponding to the center beam is also the same as the specific implementation method for calculating the local Moran index of any beam in the above embodiments. Therefore, it will not be described again here.

[0117] The specific number of loops can be adjusted according to the actual situation; for example, the number of loops can be 999.

[0118] In S602, the number of parameters whose absolute value is greater than the absolute value of the local Moran index of the beam can be counted in the number of cycles, and then the quotient of the number of parameters and the number of cycles can be determined as the significance parameter.

[0119] In the above scheme, by randomly selecting the center beam and calculating its corresponding local Moran index, it is possible to test whether the low correlation between the beam and other beams is a random phenomenon, thereby reducing the coincidence of the singular beams determined based on the correlation measurement parameter and thus improving the accuracy of the determined singular beams.

[0120] The specific implementation methods for determining the beam classification result in the above embodiments are described below. Specifically, S402 or S502 may include: S701: Obtaining the mean and variance of the correlation measurement parameter of the beam and the correlation measurement parameters of other singular beams. S702: Determining the beam classification result based on the relationship between the correlation measurement parameter of the beam and its mean and variance.

[0121] Specifically, in S702, different beam classification results represent different levels of correlation between the beam and other beams in the beam set. For example, if the beam classification results include severely singular beams, moderately singular beams, and slightly singular beams, then severely singular beams indicate extremely low correlation between the beam and other beams in the beam set; moderately singular beams indicate relatively low correlation; and slightly singular beams indicate slightly low correlation. Different classifications can be assigned different frequency weights; the closer the classification is to a singular beam, the greater the frequency weight.

[0122] The following example illustrates one implementation method for determining beam classification results based on the relationship between correlation metrics and the mean and variance. Please refer to Table 1, which shows the beam classification results. This represents the correlation metric parameter of the beam. This represents the mean of the correlation metric parameters of this beam and the correlation metric parameters of other singular beams. This represents the variance of the correlation metric of this beam and the correlation metric of other singular beams.

[0123]

[0124] Table 1 Beam classification results

[0125] Furthermore, regarding conditions one through eight in the above embodiments: For conditions one and five, the three beam classification results (severely singular beam, moderately singular beam, and mildly singular beam) all characterize the beam as a singular beam, therefore the beam can be identified as a singular beam; in this example, if the beam classification results do not directly identify a singular beam, then the beam is not a singular beam; for condition two, in addition to the above beam classification results, in other classification examples, it is necessary to further determine whether the beam's correlation measurement parameter is less than the first threshold, thereby determining whether the beam is a singular beam; for condition three, based on the above beam classification results, it is necessary to further determine whether the number of times the beam is identified as the first candidate singular beam is greater than the second threshold, thereby determining whether the beam is a singular beam; for condition... Fourth, it can be further determined whether the sum of the above-mentioned frequency weights and the number of times the beam is identified as the second candidate singular beam is greater than the third threshold, thereby determining whether the beam is a singular beam; for condition six, in addition to the above beam classification results, in other classification examples, it is necessary to further determine whether the beam's correlation measurement parameter is less than the first threshold and whether the significance parameter is less than the fourth threshold, thereby determining whether the beam is a singular beam; for condition seven, based on the above beam classification results, it is necessary to further determine whether the number of times the beam is identified as the first candidate singular beam is greater than the fifth threshold, thereby determining whether the beam is a singular beam; for condition eight, it can be further determined whether the sum of the above-mentioned frequency weights and the number of times the beam is identified as the second candidate singular beam is greater than the sixth threshold, thereby determining whether the beam is a singular beam.

[0126] In the above scheme, singular beams can be classified based on the mean and variance of the correlation measurement parameters. Since the mean and variance of the correlation measurement parameters reflect the distribution of the correlation measurement parameters of each beam, beams with extremely low correlation and slightly low correlation can be distinguished, thereby improving the accuracy of the identified singular beams.

[0127] The specific implementation method for determining the adjacent beams corresponding to a beam is described below. Specifically, before S102, the method for reconstructing beam characteristic parameters provided in this application embodiment may further include: S801: For any beam in the full beam set, determining the measured beam characteristic parameters of the neighboring beams corresponding to that beam from the observation matrix based on a set sliding window. S802: Determining the measured beam characteristic parameters of the adjacent beams from the measured beam characteristic parameters of the neighboring beams based on a set adjacency indication function.

[0128] In S801, the observation matrix includes the measured beam characteristic parameters of each beam in the full beam set, while the sliding window is used to determine the measured beam characteristic parameters of the neighboring beams corresponding to any beam from the observation matrix. Specifically, any beam can be taken as the center beam, and the other beams within the sliding window can be taken as the neighboring beams corresponding to that beam.

[0129] As one implementation method, a sliding window This can be implemented using a 3×3 matrix, that is:

[0130] .

[0131] In S802, the adjacency indicator function is used to determine whether a neighboring beam of a beam belongs to the adjacent beam of the beam. As one implementation, the adjacency rule can be set to satisfy the following conditions:

[0132] ;

[0133] in, Indicates position The adjacency set is the set of neighbors in the eight directions: up, down, left, right, and diagonal.

[0134] The adjacency indicator function can then be expressed as:

[0135] ;

[0136] in, This represents the adjacency indicator function, used to determine position. Is it The neighbors.

[0137] Optionally, based on the adjacency indicator function in the above example, all neighboring beams in the eight directions of up, down, left, right and diagonal corresponding to the beam will be identified as adjacent beams; in other cases, only some of the neighboring beams in the eight directions can be identified as adjacent beams.

[0138] In the above scheme, neighboring beams are determined from the observation matrix based on a set sliding window, and neighboring beams are determined from the neighboring beams based on a set adjacency indicator function. This allows for quick finding of the neighboring beams of the beam in the full beam set, which in turn facilitates the determination of the correlation metric parameters corresponding to the beam.

[0139] Optionally, based on the above embodiments, for a beam located at the edge of the observation matrix, its adjacent beams include supplementary beams and beams located within the sliding window in the observation matrix, and the measured beam characteristic parameters of the supplementary beam are the average of the measured beam characteristic parameters of the neighboring beams in the sliding window.

[0140] In the above scheme, for beams located at the edge of the observation matrix, since the number of neighboring beams within the sliding window is less than that of other beams, the number of neighboring beams of each beam can be made consistent by adding supplementary beams, and the accuracy of the correlation measurement parameters of beams located at the edge of the observation matrix can be improved.

[0141] Please refer to Figure 2 , Figure 2 This is a schematic diagram of an observation matrix provided in an embodiment of this application. Figure 2 Solid circles represent supplementary beams, hollow circles represent beams in the full beam set, the center number represents the beam identifier corresponding to the beam, dashed boxes represent sliding windows, and arrows indicate the sliding direction.

[0142] by Figure 2 Taking the observation matrix shown as an example, and assuming that the measured beam characteristic parameter is RSRP and the correlation metric parameter is the local Moran index, the specific calculation process of a method for reconstructing beam characteristic parameters provided in this application embodiment is described below.

[0143] Beam Collection Corresponding observation matrix It can be represented as:

[0144] ;

[0145] in, express The mid-beam designation is The RSRP of the beam, which is located within the matrix. OK List, Indicates the row number of the observation matrix. This indicates the number of columns in the observation matrix.

[0146] Based on sliding window Regarding the above observation matrix any position in Extract to Submatrix of the center beam submatrix This includes the aforementioned center beam. RSRP and the center beam The RSRP of the corresponding neighboring beam.

[0147] Based on the set adjacency indication function Determine the center beam from the RSRPs of the neighboring beams mentioned above. RSRP of adjacent beams, where the center beam The RSRP of a beam and the RSRP of its adjacent beam can be expressed as: :

[0148] .

[0149] The local Moran index can be calculated using the following formula:

[0150] ;

[0151] in, Indicates position The local Moran index corresponding to the beam. This indicates the RSRP corresponding to this beam. This represents the average RSRP of the beam and the RSRP of its adjacent beams.

[0152] ;

[0153] This represents the variance of the RSRP of the current beam and the RSRP of its adjacent beam.

[0154] ;

[0155] express middle Position weight.

[0156] For the beams with a local Moran index less than 0, to verify their local Moran index... The statistical significance of the center point can be determined using a nonparametric permutation test based on a computational window. When performing the significance test, the center point... Together with all values ​​in its neighborhood, it forms the set to be replaced. In each permutation, a value is randomly selected as the center, and the rest are considered as the neighborhood, thus calculating the local Moran index under the permutation:

[0157] ;

[0158] in, Indicates after permutation Middle position RSRP of the beam, Indicates the first The second loop and The range of values ​​is , Indicates the number of iterations.

[0159] Then, the original local Moran exponent is calculated using the indicator function. The extreme statistic during the permutation process – the empirical p-value:

[0160] ;

[0161] in, This indicates an indicator function that takes the value 1 when the condition is true and 0 otherwise.

[0162] The final significance result can be expressed as:

[0163] ;

[0164] in, It is the significance level threshold (usually taken as 0.05). If the result is 1, then the local Moran index at that location is significant, and the beam can be considered as the first candidate singular beam.

[0165] Define a set The beam identifiers corresponding to the beams that pass the saliency test are added to this set. middle:

[0166] ;

[0167] statistics The sets corresponding to multiple times Multiple sets are obtained. The number of times each beam identifier appears is counted. If the number of occurrences for a certain beam is greater than the third threshold, then that beam can be considered a second candidate singular beam.

[0168] Let the set of local Moran indices of the singular beam be . The definition is as follows:

[0169] ;

[0170] Calculate the mean of the local Moran indices for all singular beams. With variance :

[0171] ;

[0172] ;

[0173] in, For set The total number of elements.

[0174] The classification method is shown in Table 1. When a beam is judged as a severely singular beam, it is assigned a frequency weight. A score of 5 means that the beam was identified as a singular beam five times. This process continues until the final weighting of the beam's score is determined. The number of times the beam was identified as the second candidate singular beam If the sum of the above values ​​is greater than the sixth threshold, then the beam is identified as a singular beam.

[0175] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating a method for reconstructing beam characteristic parameters applied to a user terminal, as provided in an embodiment of this application. In this case, the reconstruction model is deployed on the user terminal. The method for reconstructing beam characteristic parameters may include: S901: Receiving reference signals for 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 signals. S903: Reconstructing the predicted beam characteristic parameters of other beams in the full beam set, excluding the beam subset, based on the measured beam characteristic parameters of each beam in the beam subset.

[0176] The network device stores the beam identifiers of each beam in the beam subset. The network device can send reference signals of each beam in the beam subset to the user terminal. The beam subset includes singular beams whose correlation with other beams is below a correlation threshold. After receiving the reference signals of each beam in the beam subset sent by the network device, the user terminal obtains the measured beam characteristic parameters of each beam in the beam subset based on the reference signals.

[0177] In S903, the terminal can reconstruct the predicted beam characteristic parameters of other beams in the entire beam set, excluding 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 can be input into the reconstruction model to obtain the predicted beam characteristic parameters of other beams in the entire beam set, excluding the beam subset, output by the reconstruction model. In some cases, the output of the reconstruction model may include not only the predicted beam characteristic parameters of other beams in the entire beam set, excluding the beam subset, but also the predicted beam characteristic parameters of the beam subset.

[0178] In the above scheme, when reconstructing the predicted beam characteristic parameters of other beams in the full beam set (excluding the beam subset) using the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation to other beams in the full beam set can be added to the beam subset. This allows the beam characteristic parameters of the singular beams to be obtained through measurement even if the beam characteristic parameters cannot be obtained through reconstruction. Therefore, the method provided in this application can obtain the beam characteristic parameters of highly correlated beams through reconstruction, and also obtain the beam characteristic parameters of less correlated beams through measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

[0179] Optionally, based on the above embodiments, the method for reconstructing beam characteristic parameters provided in this application may further include a step of determining singular beams. As one implementation, the step of determining singular beams can be performed after the user terminal establishes a connection with the network device, and the step of reconstructing beam characteristic parameters can be performed only when data transmission is required. In this case, the step of determining singular beams is performed before the step of reconstructing beam characteristic parameters.

[0180] As another implementation, the step of determining the singular beam can be performed periodically or as needed (e.g., when the communication environment changes significantly). In this case, 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, then 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, then the next step of reconstructing the beam characteristic parameters can be performed based on the newly determined singular beam.

[0181] The method for reconstructing beam characteristic parameters provided in this application embodiment may further include: S1001: receiving reference signals of each beam in the beam set transmitted by the network device at different times. S1002: for any given time, measuring the measured beam characteristic parameters of each beam in the beam set based on the reference signals transmitted at that time. S1003: determining the singular beams in the beam set according to the measured beam characteristic parameters corresponding to at least one time. S1004: sending beam information corresponding to the singular beams to the network device.

[0182] The specific implementations of S1001 and S1002 are the same as those of S101, and the specific implementation of S1003 is the same as that of S102, so they will not be described again here.

[0183] In S1004, the present application embodiment does 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 identified as a candidate singular beam, the correlation measurement parameters of the singular beam, etc.

[0184] As one implementation method, the terminal can send beam information corresponding to a singular beam to the network device via 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 identified as a candidate singular beam, and numberOfOddBeams = M is used to send the number of singular beams.

[0185] In the above scheme, since the beam reconstruction process is the process of reconstructing the predicted beam characteristics of the entire beam set by measuring the beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the entire beam set can be determined based on the measured beam characteristic parameters of the beam, thereby identifying the singular beam with higher accuracy.

[0186] Furthermore, based on the above embodiments, S903 may include: inputting the measured beam feature parameters of each beam in the beam subset into the pre-trained reconstruction model to obtain the predicted beam feature parameters, wherein the arrangement order of each beam in the observation matrix is ​​consistent with the arrangement order of each beam in the beam matrix when training the reconstruction model.

[0187] The implementation methods corresponding to the observation matrix have been described in detail in the above embodiments, and will not be repeated here.

[0188] In the above scheme, since the purpose of the method provided in this application embodiment is to find beams that may not be available during the reconstruction process due to low correlation from the full beam set, the arrangement order of each beam in the observation matrix is ​​set to be consistent with the arrangement order of each beam in the beam matrix when training the reconstruction model, so that the adjacent beams of the beams in the two processes are consistent, thereby improving the accuracy of the determined singular beams.

[0189] Furthermore, based on the above embodiments, a singular beam decision maker (for determining singular beams) and a Moran index calculator (for calculating local Moran indices) may be deployed on the user terminal; an optimized pattern generator (for updating beam subsets) may be deployed on the network device.

[0190] Please refer to Figure 4 , Figure 4This is an interactive diagram illustrating a first beam determination method provided in an embodiment of this application. The beam determination method may include: S1101: A network device sends reference signals for each beam in the full beam set to a user terminal at different times. S1102: The user terminal receives the reference signals for each beam in the full beam set sent by the network device at different times. S1103: For any given time, the user terminal measures the measured beam characteristic parameters of each beam in the full beam set based on the reference signals sent at that time. S1104: The user terminal determines the singular beams in the full beam set based on the measured beam characteristic parameters corresponding to at least one time. S1105: The user terminal sends 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 reference signals for each beam in the beam subset to the user terminal. S1109: The user terminal receives reference signals for each beam in the beam subset sent by the network device. S1110: The user terminal measures the measured beam characteristic parameters of each beam in the beam subset based on the reference signals. S1111: The user terminal reconstructs the predicted beam characteristic parameters of the other beams in the full beam set, excluding the beam subset, based on the measured beam characteristic parameters of each beam in the beam subset.

[0191] Please refer to Figure 5 , Figure 5 This is a flowchart illustrating a method for reconstructing beam characteristic parameters applied to a network device, as provided in an embodiment of this application. In this case, the reconstruction model is deployed on the network device. The method for reconstructing beam characteristic parameters may include: S1201: Sending reference signals for each beam in a beam subset to a user terminal. S1202: Receiving measured beam characteristic parameters of each beam in the beam subset sent by the user terminal. S1203: Reconstructing predicted beam characteristic parameters of other beams in the full beam set, excluding the beam subset, based on the measured beam characteristic parameters of each beam in the beam subset.

[0192] Specifically, the network device stores the beam identifiers of each beam in the beam subset. The network device can send reference signals of each beam in the beam subset to the user terminal. The beam subset includes singular beams whose correlation with other beams is below a correlation threshold. After receiving the reference signals of each beam in the beam subset sent by the network device, the user terminal obtains the measured beam characteristic parameters of each beam in the beam subset based on the reference signals and sends these measured beam characteristic parameters to the network device.

[0193] In S1203, the network device can reconstruct the predicted beam characteristic parameters of other beams in the full beam set, excluding 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 can be input into the reconstruction model to obtain the predicted beam characteristic parameters of other beams in the full beam set, excluding the beam subset, output by the reconstruction model.

[0194] In the above scheme, during the process of reconstructing the predicted beam characteristic parameters of other beams in the full beam set (excluding the beam subset) using the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation to other beams in the full beam set can be added to the beam subset. This allows the beam characteristic parameters of the singular beams to be obtained through measurement even if they cannot be obtained through reconstruction. Therefore, the method provided in this application can obtain the beam characteristic parameters of highly correlated beams through reconstruction, and also obtain the beam characteristic parameters of less correlated beams through measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

[0195] Optionally, based on the above embodiments, before S1201, the method for reconstructing beam feature parameters provided in this application embodiment may further include: updating the beam subset based on the beam information corresponding to the singular beam.

[0196] Specifically, as one implementation, the network device can update the beam subset immediately after receiving the beam information corresponding to the singular beam; as another implementation, the network device can also not update the beam subset immediately after receiving the beam information corresponding to the singular beam, but update it before sending the beam subset to the user terminal.

[0197] This application does not specifically limit the implementation method of updating the beam subset. For example, when there is no singular beam in the original beam subset, the singular beam can be added to the beam subset; or, if a beam that was previously determined to be added to the beam subset is not determined to be a singular beam this time, the beam can be deleted from the beam subset, etc.

[0198] In the above scheme, after receiving the beam information of the singular beam sent by the user terminal, the network device does not need to add all the singular beams to the beam subset. Instead, it updates the beam subset according to the beam information of the singular beam, thereby improving the flexibility of updating the beam subset.

[0199] The following describes a specific implementation method for updating a beam subset, using beam information including beam identifier and beam occurrence count as an example. In this case, the steps of updating the beam subset based on the beam information corresponding to the singular beam may include: if the beam occurrence count is greater than the seventh threshold, then add the beam identifier of the singular beam to the beam subset.

[0200] The number of times a beam appears represents the number of times a singular beam is identified as a candidate singular beam. If the number of times a beam appears is greater than the seventh threshold, it can be considered that the singular beam has a low correlation with other beams. Therefore, the beam identifier of the singular beam can be added to the beam subset.

[0201] It should be noted that the specific value of the seventh threshold can be adjusted according to the actual situation. As one implementation method, the seventh threshold can be determined based on communication environment information, which includes at least one of the following: user speed, channel state changes, multipath effects, and whether obstruction has occurred.

[0202] In the above scheme, for beams that have been identified as singular beams, it is possible to further determine whether to add the beam identifier corresponding to the beam to the beam subset based on the size of the beam's appearance, thereby improving the flexibility of adding singular beams.

[0203] Furthermore, based on the above embodiments, the method for reconstructing beam characteristic parameters provided in this application may further include a step of determining singular beams. As one implementation, the step of determining singular beams can be performed after the user terminal establishes a connection with the network device, and the step of reconstructing beam characteristic parameters can be performed only when data transmission is required. In this case, the step of determining singular beams is performed before the step of reconstructing beam characteristic parameters.

[0204] As another implementation, the step of determining the singular beam can be performed periodically or as needed (e.g., when the communication environment changes significantly). In this case, 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, then 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, then the next step of reconstructing the beam characteristic parameters can be performed based on the newly determined singular beam.

[0205] The method for reconstructing beam characteristic parameters provided in this application embodiment may further include: S1301: sending reference signals of each beam in the beam set to the user terminal at different times. S1302: for any given time, receiving the measured beam characteristic parameters of each beam in the beam set obtained by the user terminal based on the reference signals sent at that time. S1303: determining the singular beams in the beam set based on the measured beam characteristic parameters corresponding to at least one time.

[0206] The specific implementations of S1301 and S1302 are the same as those of S101, and the specific implementation of S1303 is the same as that of S102, so they will not be described again here.

[0207] In the above scheme, since the beam reconstruction process is the process of reconstructing the predicted beam characteristics of the entire beam set by measuring the beam characteristic parameters of the beam subset, the correlation between the beam and other beams in the entire beam set can be determined based on the measured beam characteristic parameters of the beam, thereby identifying the singular beam with higher accuracy.

[0208] Please refer to Figure 6 , Figure 6 This is an interactive diagram illustrating a second beam determination method provided in an embodiment of this application. The beam determination method may include: S1401: A network device sends reference signals for each beam in the full beam set to a user terminal at different times. S1402: The user terminal receives the reference signals for each beam in the full beam set sent by the network device at different times. S1403: For any given time, the user terminal measures the measured beam characteristic parameters of each beam in the full beam set based on the reference signals sent at that time. S1404: The user terminal sends the measured beam characteristic parameters of each beam in the corresponding beam subset to the network device at that time. S1405: The network device receives the measured beam characteristic parameters of each beam in the corresponding beam subset sent by the user terminal. S1406: The network device determines the singular beams in the full beam set based on the measured beam characteristic parameters corresponding to at least one time. S1407: The network device updates the beam subset based on the beam information corresponding to the singular beams. S1408: The network device sends the reference signals for 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 measured beam characteristic parameters of each beam in the beam subset based on the reference signals. S1411: The user terminal sends the measured beam characteristic parameters of each beam in the beam subset to the network device. S1412: The network device receives the measured 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 full beam set, excluding the beam subset, based on the measured beam characteristic parameters of each beam in the beam subset.

[0209] Please refer to Figure 7 , Figure 7 This application provides a structural block diagram of an electronic device 1500, which includes at least one processor 1501, at least one communication interface 1502, at least one memory 1503, and at least one communication bus 1504. The communication bus 1504 enables direct communication between these components, the communication interface 1502 facilitates signaling or data communication with other node devices, and the memory 1503 stores machine-readable instructions executable by the processor 1501. When the electronic device 1500 is running, the processor 1501 communicates with the memory 1503 via the communication bus 1504, and the machine-readable instructions are executed by the processor 1501 when invoked.

[0210] As one implementation, the aforementioned electronic device 1500 can be a user terminal, and different user terminals can be interconnected via wired or wireless means. User terminals can be widely used in various scenarios, such as 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 grids, smart furniture, smart offices, smart wearables, smart transportation, and smart cities.

[0211] The user terminal may also be referred to as a mobile station (MS), terminal, or terminal equipment, and may further include a subscriber unit, cellular phone, smartphone, wireless data card, personal digital assistant (PDA) computer, tablet computer, handheld modem, laptop computer, cordless phone, wireless local loop (WLL) station, machine type communication (MTC) terminal, etc. For ease of description, in all embodiments of this application, the devices mentioned above are collectively referred to as user terminals.

[0212] The aforementioned user terminal may further include an antenna and a transceiver. The transceiver modulates (e.g., analog-to-digital conversion, filtering, amplification, and up-conversion) the output sample and generates an uplink signal, which 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 modulates (e.g., filtering, amplification, down-conversion, and digitization) the signal received from the antenna and provides input sampling. The processor 1501 is used to execute the method for reconstructing beam characteristic parameters or the beam determination method described in the above embodiments. The embodiments of this application do not limit the specific technology or device form used in the user terminal.

[0213] In another implementation, the aforementioned electronic device 1500 can be a network device, and the user terminal can connect to the network device wirelessly. The network device can also connect to or transmit and receive information with Evolved Universal Terrestrial Radio Access (E-UTRA), New Radio (NR), and future radio access systems or WiFi systems as defined in the 3rd Generation Partnership Project (3GPP). The network device can also connect to devices from two or more of the aforementioned different radio access systems. The network device can also connect to an Open Radio Access Network (O-RAN).

[0214] Network equipment may be configured with modules for implementing base station functions. These modules can perform the functions of: base station, evolved NodeB (eNodeB or eNB), transmission reception point (TRP), next-generation NodeB (gNB) in 5G mobile communication systems, next-generation base station in 6G mobile communication systems, base station in future mobile communication systems, or access node in WiFi systems.

[0215] The aforementioned base station may also include an antenna and a transceiver. In the uplink, the uplink signal from the user terminal is received via the antenna, mediated 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, mediated 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 characteristic parameters or the beam determination method as 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.

[0216] It is understood that the above only describes a simplified design of the base station. In practical applications, the base station may include any number of transmitters, receivers, processors, controllers, memory, communication units, etc., and all base stations that can implement this application are within the protection scope of this application.

[0217] The processor 1501 comprises one or more, and can be an integrated circuit chip with signal processing capabilities. The 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 special-purpose 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, or discrete hardware components. Furthermore, when there are multiple processors 1501, some can be general-purpose processors, and others can be special-purpose processors.

[0218] The memory 1503 includes one or more, which may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0219] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for reconstructing beam characteristic parameters, characterized in that, Applied to a user terminal, the method includes: The system receives reference signals from each beam in a beam subset sent by a network device. The beam subset includes singular beams, and the correlation between the singular beams and other beams in the full beam set to which the beam subset is located is lower than a correlation threshold. The beam subset also includes beams whose correlation with other beams in the full beam set is not lower than the correlation threshold. The measurement beam characteristic parameters of each beam in the beam subset are measured based on the reference signal; Based on the measured beam characteristic parameters of each beam in the beam subset, the predicted beam characteristic parameters of other beams in the full beam set, excluding the beam subset, are reconstructed. The beam subset is obtained by updating the beam subset based on the beam information corresponding to the singular beam by the network device, and the measured beam characteristic parameters of the singular beam are used to select the optimal beam.

2. The method according to claim 1, characterized in that, The method further includes: Receive reference signals of each beam in the full beam set sent by the network device at different times; For any given moment, the measurement beam characteristic parameters of each beam in the beam set are measured based on the reference signal transmitted at that moment; The singular beam in the full beam set is determined based on the measured beam characteristic parameters corresponding to at least one time moment; Send the beam information corresponding to the singular beam to the network device.

3. The method according to claim 2, characterized in that, Determining the singular beam in the full beam set based on the measured beam characteristic parameters corresponding to at least one time moment includes: For any beam in the full beam set at any given time, the correlation metric parameter between the beam and the adjacent beam is determined based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beam. Whether a beam is the singular beam is determined based on a correlation metric between the beam and the adjacent beam at at least one time point.

4. The method according to claim 3, characterized in that, The step of determining the singular beam in the full beam set based on the measured beam characteristic parameters corresponding to at least one time moment further includes: For any beam in the full beam set at any given time, the saliency parameter of the beam is determined based on the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams corresponding to the beam. Accordingly, determining whether a beam is the singular beam based on a correlation metric parameter between the beam and the adjacent beam at at least one time includes: Whether a beam is the singular beam is determined based on the correlation metric parameter between the beam and the adjacent beam at at least one time point and the significance parameter. or, Determining whether a beam is the singular beam based on a correlation metric parameter between the beam and the adjacent beam at at least one time point includes: For any given moment, the beam classification result of the beam is determined based on the correlation metric parameters of the beam and the correlation metric parameters of other beams in the full beam set. Whether a beam is the singular beam is determined based on the beam classification result and the correlation metric parameter of the beam.

5. A method for reconstructing beam characteristic parameters, characterized in that, Applied to network devices, the method includes: Reference signals of each beam in the beam subset are sent to the user terminal. The beam subset includes singular beams, and the correlation between the singular beams and other beams in the full beam set to which the beam subset is located is lower than a correlation threshold. The beam subset also includes beams whose correlation with other beams in the full beam set is not lower than the correlation threshold. Receive the measured beam characteristic parameters of each beam in the beam subset sent by the user terminal; Based on the measured beam characteristic parameters of each beam in the beam subset, the predicted beam characteristic parameters of other beams in the full beam set, excluding the beam subset, are reconstructed. Before transmitting the reference signals of each beam in the beam subset to the user terminal, the method further includes: The beam subset is updated based on the beam information corresponding to the singular beam; The measured beam characteristic parameters of the singular beam are used to screen the optimal beam.

6. The method according to claim 5, characterized in that, The method further includes: Reference signals of each beam in the beam set are sent to the user terminal at different times; For any given moment, receive the measured beam characteristic parameters of each beam in the full beam set obtained by the user terminal based on the reference signal sent at that moment; The singular beam in the beam set is determined based on the measured beam characteristic parameters corresponding to at least one time moment.

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

8. A method for reconstructing object feature parameters, characterized in that, include: Obtain the measurement object feature parameters of each object in the object subset, wherein the object subset includes singular objects, the singular objects have a correlation with other objects in the full set of objects to which the object subset belongs that is lower than a correlation threshold, and the object subset also includes objects whose correlation with other objects in the full set of objects is not lower than the correlation threshold; Based on the measured object feature parameters of each object in the object subset, the predicted object feature parameters of other objects in the entire object set, excluding the object subset, are reconstructed; The object subset is obtained by updating the object subset based on the object information corresponding to the singular object.

9. 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 that can be executed by the processor, and the processor can invoke the computer program instructions to perform the method as described in any one of claims 1-8.

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