CN120357974A

CN120357974A - Method for reconstructing beam characteristic parameters, method for determining beam, method for reconstructing object characteristic parameters and device

Info

Publication number: CN120357974A
Application number: CN202510813565.XA
Authority: CN
Inventors: 刘彬; 丁传垚; 曹永照; 单宝堃
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2025-06-18
Filing date: 2025-06-18
Publication date: 2025-07-22

Abstract

The present application provides a method for reconstructing beam characteristic parameters, a method for determining beams, a method for reconstructing object characteristic parameters, and a device, and relates to the field of data reconstruction technology. The method for reconstructing beam characteristic parameters applied to a user terminal includes: receiving a reference signal of each beam in a beam subset sent by a network device, wherein the beam subset includes a singular beam, and the correlation between the singular beam and other beams in the full beam set where the beam subset is located is lower than a correlation threshold; measuring the measured beam characteristic parameters of each beam in the beam subset based on the reference signal; reconstructing the predicted beam characteristic parameters of other beams in the full beam set except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset. The above method can obtain the beam characteristic parameters of beams with higher correlation by reconstruction, or obtain the beam characteristic parameters of beams with lower correlation by measurement, thereby improving the accuracy of the prediction results of the reconstructed full beam set.

Description

Method for reconstructing beam characteristic parameters, method for determining beam, method for reconstructing object characteristic parameters and device

Technical Field

The application relates to the technical field of data reconstruction, in particular to a method for reconstructing beam characteristic parameters, a method for determining a beam, a method for reconstructing object characteristic parameters and electronic equipment.

Background

Reconstruction is a task or technique that aims to reconstruct the complete form of the original data using a model, i.e., reconstruct an estimated version of the original or target data based on partial or warped information, in other words, let the model guess missing parts or restore obscured data. However, in reconstructing missing or masked data from a portion of the information, there is a portion of the prediction results that are inaccurate, resulting in a reconstructed prediction result that is less accurate.

Taking the beam reconstruction scenario as an example, the spatial domain downlink beam reconstruction can be performed on the beam corpus based on the measurement results of the beam subset, thereby reducing signaling overhead and prediction delay. However, when the prediction result of the beam corpus is reconstructed from the measurement results of the beam subset, the prediction result of some beams is inaccurate, resulting in lower accuracy of the prediction result of the reconstructed beam corpus.

Disclosure of Invention

The embodiment of the application aims to provide a method for reconstructing beam characteristic parameters, a method for determining beams, a method for reconstructing object characteristic parameters and electronic equipment, which are used for solving the technical problem that in the prior art, when a prediction result of a beam whole set is reconstructed through a measurement result of a beam subset, the accuracy of the prediction result of the reconstructed beam whole set is lower.

In a first aspect, an embodiment of the present application provides a method for reconstructing beam characteristic parameters, which is applied to a user terminal, and the method includes receiving a reference signal of each beam in a beam subset sent by a network device, where the beam subset includes a singular beam, and a correlation between the singular beam and other beams in a beam subset where 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 signal, and reconstructing predicted beam characteristic parameters of other beams in the beam subset except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

In the above scheme, in the process of reconstructing the predicted beam characteristic parameters of the other beams in the beam subset except the beam subset through the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation with the other beams in the beam subset can be added, so that even if the beam characteristic parameters corresponding to the singular beams cannot be obtained through reconstruction, the corresponding beam characteristic parameters can be obtained through measurement. Therefore, the method provided by the embodiment of the application can obtain the beam characteristic parameters of the beams with higher correlation through reconstruction, and can also obtain the beam characteristic parameters of the beams with lower correlation through measurement, thereby improving the accuracy of the prediction result of the beam corpus obtained through reconstruction.

In an optional implementation mode, the method further comprises the steps of receiving reference signals of all beams in the beam total set sent by the network equipment at different moments, measuring measured beam characteristic parameters of all beams in the beam total set based on the reference signals sent by the moment at any moment, determining singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment, and sending beam information corresponding to the singular beams to the network equipment. In the above scheme, the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam whole set through the measured beam characteristic parameters of the beam subset, so that the correlation between the beam and other beams in the beam whole set can be determined based on the measured beam characteristic parameters of the beam, and further, the singular beam with higher accuracy can be determined.

In an alternative embodiment, the determining the singular beams in the beam set according to the measured beam characteristic parameters corresponding to at least one time includes determining, for any one beam in the beam set at any time, a correlation metric parameter between the beam and an adjacent beam according to 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 according to the correlation metric parameter between the beam corresponding to at least one time and the adjacent beam. In the above scheme, the correlation measurement parameter can be used for characterizing the correlation between two objects, so that by determining the correlation measurement parameter between the beam and the adjacent beam, the singular beams with lower correlation with other beams in the beam total set can be determined more accurately.

In an alternative embodiment, the method for determining the singular beams in the beam set according to the measured beam characteristic parameters corresponding to at least one time further comprises determining, for any one of the beam set at any one time, a significance parameter of the beam according to the measured beam characteristic parameters of the beam and the measured beam characteristic parameters of the adjacent beams corresponding to the beam, and correspondingly, determining whether the beam is the singular beam according to the correlation metric parameters between the beam corresponding to at least one time and the adjacent beams, including determining whether the beam is the singular beam according to the correlation metric parameters and the significance parameters between the beam corresponding to at least one time and the adjacent beams. In the above scheme, the saliency parameter can be used for checking whether the local numerical space autocorrelation is nonrandom salient, so that the coincidence of the singular beams determined based on the correlation measurement parameter can be reduced by determining the saliency parameter of the beams, and the accuracy of the determined singular beams can be further improved.

In a second aspect, an embodiment of the present application provides a method for reconstructing beam characteristic parameters, which is applied to a network device, and the method includes sending reference signals of each beam in a beam subset to a user terminal, where the beam subset includes a singular beam, and a correlation between the singular beam and other beams in a beam subset where 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 beam subset except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

In an alternative embodiment, before the reference signal of each beam in the beam subset is sent to the user terminal, the method further comprises updating the beam subset based on the beam information corresponding to the singular beams. In the above scheme, after receiving the beam information of the singular beams sent by the user terminal, the network device may update the beam subset according to the beam information of the singular beams instead of adding all the singular beams into the beam subset, thereby improving the flexibility of updating the beam subset.

In an optional implementation mode, the method further comprises the steps of sending reference signals of all beams in the beam total set to the user terminal at different moments, receiving measured beam characteristic parameters of all beams in the beam total set, which are obtained by the user terminal and are measured based on the reference signals sent at any moment, and determining the singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment. In the above scheme, the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam whole set through the measured beam characteristic parameters of the beam subset, so that the correlation between the beam and other beams in the beam whole set can be determined based on the measured beam characteristic parameters of the beam, and further, the singular beam with higher accuracy can be determined.

In a third aspect, an embodiment of the present application provides a method for determining a beam, including obtaining a measured beam characteristic parameter obtained by a user terminal based on measurement of reference signals of each beam in a beam set sent by a network device at different times, and determining a singular beam in the beam set according to the measured beam characteristic parameter corresponding to at least one time, where a correlation between the singular beam and other beams in the beam set is lower than a correlation threshold.

In the above scheme, the singular beams with lower correlation with other beams in the beam set can be determined based on the measured beam characteristic parameters of each beam in the beam set, so that the determined singular beams can be added into the beam subset, the corresponding beam characteristic parameters can be obtained through measurement even if the beam characteristic parameters corresponding to the singular beams cannot be obtained through reconstruction, and the accuracy of the prediction result of the beam set obtained through reconstruction is improved.

In a fourth aspect, an embodiment of the present application provides a method for reconstructing object feature parameters, including obtaining measured object feature parameters of each object in a subset of objects, where the subset of objects includes a singular object, and a correlation between the singular object and other objects in a subset of objects in which the subset of objects is located is lower than a correlation threshold, and reconstructing predicted object feature parameters of other objects in the subset of objects except the subset of objects based on the measured object feature parameters of each object in the subset of objects.

In the above-mentioned scheme, in the process of reconstructing the predicted object feature parameters of the other objects in the object subset except for the object subset by using the measured object feature parameters of each object in the object subset, the singular objects having low correlation with the other objects in the object subset may be added to the object subset, so that even if the object feature parameters corresponding to the singular objects cannot be obtained by reconstruction, the corresponding object feature parameters can be obtained by measurement. Therefore, the method provided by the embodiment of the application can obtain the object characteristic parameters of the object with higher correlation through reconstruction, and also can obtain the object characteristic parameters of the object with lower correlation, thereby improving the accuracy of the prediction result of the object corpus obtained through reconstruction.

In a fifth aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a bus, where the processor and the memory complete communication with each other through the bus, where the memory stores computer program instructions executable by the processor, and where the processor invokes the computer program instructions to perform a method according to the first aspect, the second aspect, the third aspect, or the fourth aspect.

Specifically, if the electronic device is a user terminal, the electronic device performs the method according to the first aspect, the third aspect or the fourth aspect, and if the electronic device is a network-side device, the electronic device performs the method according to the second aspect, the third aspect or the fourth aspect.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, which are only illustrative of some embodiments of the present application, and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings for a person skilled in the art.

Fig. 1 is a flowchart of a method for determining a beam according to an embodiment of the present application;

FIG. 2 is a schematic diagram of an observation matrix according to an embodiment of the present application;

Fig. 3 is a flowchart of a method for reconstructing beam characteristic parameters applied to a user terminal according to an embodiment of the present application;

Fig. 4 is an interaction diagram of a first method for determining a beam according to an embodiment of the present application;

Fig. 5 is a flowchart of a method for reconstructing beam characteristic parameters applied to a network device according to an embodiment of the present application;

Fig. 6 is an interaction diagram of a second method for determining a beam according to an embodiment of the present application;

Fig. 7 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

When reconstructing the original or target data of the object complete set through the partial information of the object subset, if the prediction result of a part of the objects is inaccurate, the accuracy of the prediction result of the reconstructed objects is lower. Therefore, singular objects with low correlation with other objects in the object set can be added in the object subset, so that accuracy of prediction results of the reconstructed objects can be improved. In view of the above, the embodiment of the application provides a method for reconstructing object feature parameters, which can comprise the steps of 1, acquiring measured object feature parameters of each object in an object subset, and 2, reconstructing predicted object feature parameters of other objects in a whole object set except the object subset based on the measured object feature parameters of each object in the object subset.

Specifically, the object subset includes singular objects, and the relevance between the singular objects and other objects in the object subset is lower than a relevance threshold, so that in the process of reconstructing predicted object feature parameters of other objects in the object subset except for the object subset based on measured object feature parameters of the objects in the object subset, the object feature parameters of the object with higher relevance can be obtained through reconstruction, and the object feature parameters of the object with lower relevance can be directly obtained.

Wherein, before the step 1, the step of determining the singular objects in the object corpus may be performed. It should be noted that, in the embodiment of the present application, the specific implementation manner of the singular objects in the determined object corpus is not specifically limited, and those skilled in the art may perform appropriate adjustment according to actual situations. For example, whether the object is a singular object may be determined by calculating a correlation metric parameter for each object in the object set, or whether the object is a singular object may be determined by calculating a correlation metric parameter for each object in the object set, determining a saliency parameter for each object, and the like.

In addition, the method of the embodiment of the application can be applied to various scenes, for example, the object can be a beam, a user terminal, a sensor, an image pixel point and the like.

Taking the object as a beam as an example, in beam management, the user terminal needs to determine the best beam based on the received signal reference Power (RECEIVED SIGNAL REFERENCE Power, RSRP) of the beam, but the network terminal needs to consume more resources to transmit the reference signals of all the beams to the user terminal. Therefore, the reconstruction method can be adopted, and the network equipment only needs to send the reference signals corresponding to the beam subsets to the user terminal, and does not need to send the reference signals corresponding to the beam complete sets, so that the resource cost can be reduced. Specifically, the user terminal may receive a reference signal of each beam in the beam subset sent by the network device, measure a measured beam characteristic parameter (e.g., RSRP, etc.) of each beam in the beam subset based on the reference signal, and then reconstruct a predicted beam characteristic parameter of the other beams in the beam subset except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

Taking an object as a user terminal for example, in wireless communication deployment and optimization, an operator wants to know the propagation condition (i.e. a wireless coverage map) of a Signal of a network device in space, but measurement points of the user terminal are sparse and uncontrollable, and channel information of a continuous geographic area is not fully observable, so that a reconstruction method can be adopted, the network device only needs to acquire measurement terminal characteristic parameters (such as a user terminal position, a Signal-to-Noise Ratio (SNR) and the like) of each terminal in a terminal set, and does not need to acquire measurement terminal characteristic parameters of each terminal in the terminal set, so that reconstruction of a spatial Signal field can be realized based on information reported by a small amount of terminals. Specifically, the network device may obtain measured terminal characteristic parameters of each terminal in the terminal subset, and then reconstruct predicted terminal characteristic parameters of other terminals in the terminal subset based on the measured terminal characteristic parameters of each terminal in the terminal subset.

Taking an object as a sensor as an example, in an atmospheric environment monitoring and urban sensing system, the sensor (such as a gas sensor, a temperature sensor, a humidity sensor and the like) is arranged on a plurality of fixed points of the city. Under the condition of sparse deployment, most areas in the city are unobserved, so that a reconstruction method can be adopted, and only the measured sensor characteristic parameters (such as the observed values of the sensors) of each sensor in the distributed sensor subset are required to be obtained, and the measured sensor parameters of each sensor in the sensor whole set are not required to be obtained, so that the environmental values of other spatial positions can be predicted based on a small number of known observed points, and a complete pollution thermodynamic diagram or a temperature map can be obtained. Specifically, the electronic device may obtain measured sensor characteristic parameters for each sensor in the subset of sensors and then reconstruct predicted sensor characteristic parameters for other sensors in the full set of sensors except for the subset of sensors based on the measured sensor characteristic parameters for each sensor in the subset of sensors.

In the above-mentioned scheme, in the process of reconstructing the predicted object feature parameters of the other objects in the object subset except for the object subset by using the measured object feature parameters of each object in the object subset, the singular objects having low correlation with the other objects in the object subset may be added to the object subset, so that even if the object feature parameters corresponding to the singular objects cannot be obtained by reconstruction, the corresponding object feature parameters can be obtained by measurement. Therefore, the method provided by the embodiment of the application can obtain the object characteristic parameters of the object with higher correlation through reconstruction, and can obtain the object characteristic parameters of the object with lower correlation, thereby improving the accuracy of the prediction result of the object corpus obtained through reconstruction.

A specific embodiment of the method for reconstructing the characteristic parameters of the object will be described in detail below by taking the object as a beam as an example. It can be appreciated that the following specific embodiments may be applicable to other objects, and those skilled in the art may perform corresponding substitutions, which are not repeated herein in the embodiments of the present application, for example, the beam set may be replaced by another object set, the beam characteristic parameter of the beam may be replaced by an object characteristic parameter corresponding to another object, etc.

Before describing the scheme provided in this embodiment, a training process of the reconstruction model in beam management will be described. With the trained reconstruction model, the spatial domain downlink beam prediction for beam Set a (Set a) can be performed based on the measurement results of beam Set B (Set B).

Specifically, set B is a smaller Set than Set A (i.e., set B is a subset of Set A), and Set B and Set A may be sets of reference signals, e.g., set B may be a Set of channel state Information reference signals (CHANNEL STATE Information-REFERENCE SIGNAL, CSI-RS) or a Set of synchronization signal blocks (Synchronization Signal Block, SSB), and Set A may be a Set of CSI-RSs. For example, set B includes CSI-RS# [2,4,6,8] (i.e., CSI-RS corresponding to beams with beam identifications of 2,4,6,8, respectively), and Set A includes CSI-RS# [1,2,3,4,5,6,7,8] (i.e., CSI-RS corresponding to beams with beam identifications of 1,2,3,4,5,6,7,8, respectively).

The base station selects Set B from Set A and sends the Set B to the user terminal, the terminal measures the Set B to obtain the RSRP measured value of the Set B, the RSRP measured value is used as the input or part of the input of the reconstruction model, the reconstruction model is used for predicting the RSRP of the Set A, and the training process is repeatedly executed to obtain the trained reconstruction model. By using the trained reconstruction model, the RSRP of the Set A can be obtained based on the RSRP reconstruction of the Set B. If the above-mentioned reconstruction model is not adopted, the base station needs to send the whole Set A to the terminal, and the resource cost will increase, whereas if the above-mentioned reconstruction model is adopted, the base station only needs to send the Set B, namely a part of Set A, to the terminal, and the resource cost can be reduced.

For example, set A includes 64 beams, 4 beams (i.e., beam { #1, 3, 5, 7 }) in Set A are selected as Set B through CSI-reportConfig #1 configuration, and the terminal obtains RSRP { #1, 3, 5, 7} after measuring Set B, and then outputs predicted values of RSRP of 8 reference signals in Set A or outputs probability that each reference signal in Set A has the largest RSRP measured value in Set A, i.e., predicted probability, as input of the reconstruction model.

Based on the training process and the beam reconstruction process of the reconstruction model, if a beam with low correlation with other beams exists in Set a, in the process of reconstructing the RSRP corresponding to Set a by using the RSRP corresponding to Set B, a situation may occur that the RSRP corresponding to the part of the beam cannot be obtained by reconstruction, so that the accuracy of the RSRP corresponding to the reconstructed Set a is low.

In view of this, the embodiment of the application provides a method for reconstructing beam characteristic parameters and a method for determining a beam. The method for reconstructing beam characteristic parameters is used for reconstructing predicted beam characteristic parameters of each beam in the beam total set based on measured beam characteristic parameters of each beam in the beam subset, and is different from the beam reconstruction process in the prior art in that the beam subset in the method for reconstructing beam characteristic parameters provided by the embodiment of the application comprises singular beams (i.e. beams with low correlation with other beams), and the method for determining the beams is used for determining the singular beams.

It will be appreciated that the singular beams in the method of reconstructing the beam characteristic parameters may be determined using a beam determination method, and that the singular beams determined by the beam determination method may be used in the method of reconstructing the beam characteristic parameters, or may be used for other purposes.

It should be noted that, the method for reconstructing beam characteristic parameters may be performed by a user terminal or a network device. If the method for reconstructing the beam characteristic parameters is executed by the user terminal, the reconstruction model is deployed on the user terminal, and if the method for reconstructing the beam characteristic parameters is executed by the network device, the reconstruction model is deployed on the network device. Similarly, the beam determining method described above may be performed by either the user terminal or the network device.

For example, as a first embodiment, the method of reconstructing beam characteristic parameters and the method of determining beams are performed by a user terminal, as a second embodiment, the method of reconstructing beam characteristic parameters and the method of determining beams are performed by a network device, as a third embodiment, the method of reconstructing beam characteristic parameters is performed by a user terminal, the method of determining beams is performed by a network device, and as a fourth embodiment, the method of reconstructing beam characteristic parameters is performed by a network device, and the method of determining beams is performed by a user terminal.

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

Referring to fig. 1, fig. 1 is a flowchart of a beam determining method according to an embodiment of the present application, where the beam determining method may be performed by a ue or a network device. The method for determining the beam can comprise the following steps:

S101, acquiring measured beam characteristic parameters obtained by measuring reference signals of each beam in a beam total set transmitted by a user terminal at different moments based on network equipment. S102, determining singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment.

The network equipment stores the beam identification of each beam in the beam total set, and can send the reference signals of each beam in the beam total set to the user terminal, which can obtain the measured beam characteristic parameters of each beam in the beam total set based on the reference signals after receiving the reference signals of each beam in the beam total set sent by the network equipment.

The embodiments of the present application do not specifically limit the forms of the reference signal and the measured beam characteristic parameter, and the measured beam characteristic parameter is used to screen the optimal beam, so any parameter that can reflect whether the beam is optimal may be used, for example, RSRP, an arrival angle of the beam, a beam width, and the like. The reference signals are used for measuring the measurement beam characteristic parameters, so that different reference signals can be matched based on different measurement beam characteristic parameters, and a person skilled in the art can perform appropriate adjustment according to practical situations, for example, the reference signals can comprise SSB, CSI-RS and the like.

Optionally, the terminal may measure different measured beam characteristic parameters based on the reference signals of each beam in the beam total set sent by the network device at different times. The different time may include a time when the ue switches to the access cell, a time when the communication environment changes, a time when the location of the ue changes, and so on. Therefore, the beam total set may correspond to a plurality of sets of measurement beam characteristic parameters, where each set includes measurement beam characteristic parameters measured by the terminal based on reference signals of each beam in the beam total set sent by the network device at a certain moment.

If the method for determining the beam provided by the embodiment of the application is executed by the user terminal, the terminal receives the reference signals of each beam in the beam total set sent by the network device at different moments, and measures the measured beam characteristic parameters of each beam in the beam total set based on the reference signals sent at any moment, thereby obtaining the measured beam characteristic parameters in S101.

If the method for determining the beam provided by the embodiment of the application is executed by the network device, the network device sends the reference signals of each beam in the beam total set to the user terminal at different moments, and receives the measured beam characteristic parameters of each beam in the beam total set, which are obtained by the user terminal based on the reference signals sent at the moment, for any moment, so as to obtain the measured beam characteristic parameters in S101.

In S102, a singular beam refers to a beam having a correlation with other beams in the total set of beams below a correlation threshold. The form and the value of the correlation threshold are not particularly limited, and may be one value or a set of a plurality of values, and the magnitude of the value may be adjusted according to practical situations.

For S102, as an implementation manner, singular beams in the beam set may be determined based on the measured beam characteristic parameters corresponding to one time, for example, a correlation metric parameter corresponding to a beam is determined based on the measured beam characteristic parameters corresponding to one time, so as to determine whether the beam is a singular beam, or a correlation metric parameter corresponding to a beam is determined based on the measured beam parameters corresponding to one time, so as to determine a significance parameter corresponding to a beam, so as to determine whether the beam is a singular beam.

For S102, as another embodiment, singular beams in the beam set may be determined based on the measured beam characteristic parameters corresponding to the multiple times, for example, the correlation metric parameters corresponding to the beams may be determined based on the measured beam characteristic parameters corresponding to the multiple times, and the correlation metric parameters may be counted to determine whether the beams are singular beams, or the correlation metric parameters corresponding to the beams may be determined based on the measured beam characteristic parameters corresponding to the multiple times, and the significance parameters corresponding to the beams may be determined, and the significance parameters may be counted to determine whether the beams are singular beams.

Four specific embodiments for determining singular beams are described below. The method comprises the steps of determining a singular beam by using a correlation measurement parameter, determining the singular beam by using the correlation measurement parameter and a significance parameter, determining the singular beam by using the correlation measurement parameter and a beam classification result, and determining the singular beam by using the correlation measurement parameter, the significance parameter and the beam classification result.

A first specific embodiment of determining the singular beam is described below, i.e. determining the singular beam using the correlation metric parameters, where S102 may include determining, for any one of the total set of beams at any one time, the correlation metric parameters 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 to which the beam corresponds, S201. S202, determining whether the beam is a singular beam according to the correlation measurement parameter between the beam corresponding to at least one moment and the adjacent beam.

In S201, the adjacent beam corresponding to the beam is a beam having a small distance from the beam in the total set of beams. The meaning expressed by the distance between two beams in the beam total set is not particularly limited in the embodiment of the application, for example, the distance can represent the distance between two beams in a beam matrix and the like. Wherein the beam matrix comprises a plurality of beams arranged in a certain order.

For different distance representation modes, the embodiment of the application does not limit the specific implementation manner of determining the adjacent beams of the beams, for example, the physical distance between two beams can be calculated by using the beam arrival angle and the adjacent beams can be found, or the adjacent beams can be found from the beam matrix by using a sliding window, etc.

The correlation metric parameter between a beam and its neighboring beam is used to characterize the correlation between the beam and its neighboring beam. The form of the correlation measurement parameter is not particularly limited in the embodiment of the present application, for example, the correlation measurement parameter may be one of a local morganian index of beams, a correlation coefficient between beams, and a full-beam correlation matrix.

For different correlation metric parameters, the specific implementation manner of determining the correlation metric parameters is not specifically limited, for example, the correlation metric parameters may be determined by calculating a local moland index of a beam, a correlation coefficient between beams, or a full-beam correlation matrix.

It should be noted that S201 may be performed once for each beam in the beam total set at one time, and S201 may be performed multiple times for each time (the number of times of execution may be the same as the number of beams in the beam total set).

In S202, as one embodiment, singular beams in the beam set may be determined based on the correlation metric parameters corresponding to one time, for example, the correlation metric parameters corresponding to the beam may be determined based on the measured beam characteristic parameters corresponding to one time, and thus, whether the beam is a singular beam may be determined, as another embodiment, singular beams in the beam set may be determined based on the correlation metric parameters corresponding to a plurality of time, for example, the correlation metric parameters corresponding to the beam may be determined based on the measured beam characteristic parameters corresponding to a plurality of time, and statistics may be performed on the correlation metric parameters, thereby determining whether the beam is a singular beam.

The following description of the specific embodiment of S202 illustrates that it may be determined whether the beam is a singular beam by determining whether the beam satisfies any of the following conditions, where if the beam satisfies any of the following conditions, the beam is characterized as a singular beam:

and under the condition II, the correlation measurement parameter is smaller than a first threshold value, wherein the correlation threshold value is the first threshold value.

Specifically, a correlation metric parameter smaller than the first threshold may be considered less correlated between the beam and the adjacent beam, and thus the beam may be determined to be a singular beam. The specific value of the first threshold may be adjusted according to practical situations, for example, when the correlation metric parameter is a local moland parameter, the first threshold may be 0.

When determining the singular beams in the total set of beams based on the correlation metric parameters corresponding to one time, if one of the beams in the total set of beams satisfies the condition two at the time, the beam may be determined to be the singular beam, and when determining the singular beams in the total set of beams based on the correlation metric parameters corresponding to a plurality of times, if one of the beams in the total set of beams satisfies the condition two at any time, the beam may be determined to be the singular beam.

And a ninth condition that the number of times the beam is determined to be the first candidate singular beam according to the second condition based on the plurality of measured beam characteristic parameters is greater than a second threshold, wherein the correlation threshold comprises the second threshold.

Specifically, the first candidate singular beam refers to that, based on the measured beam characteristic parameter of the beam at a certain time, the beam can be determined as a singular beam according to the second condition, and then the beam can be regarded as the first candidate singular beam at the certain time. That is, for any time, a correlation metric parameter of a beam may be determined according to a corresponding measured beam characteristic parameter, and then, whether the beam is a singular beam at the time may be determined according to the condition two and the correlation metric parameter, and if the beam is a singular beam at the time, the beam is determined to be a first candidate beam.

The number of times of the first candidate singular beam is determined by counting the measured beam characteristic parameters respectively corresponding to the plurality of times according to the condition two, wherein the number of times is larger than the second threshold value, the beam can be determined as the singular beam because the correlation between the beam and the adjacent beam is considered to be smaller. The specific value of the second threshold can be adjusted according to actual conditions.

In the above scheme, the correlation measurement parameter can be used for characterizing the correlation between two objects, so that by determining the correlation measurement parameter between the beam and the adjacent beam, the singular beams with lower correlation with other beams in the beam total set can be determined more accurately.

A second specific embodiment of determining the singular beam is described below, i.e. determining the singular beam using the correlation metric parameter and the significance parameter, where S102 may include:

S301, determining a correlation measurement parameter between a beam and an adjacent beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam aiming at any beam in the beam total set at any moment. S302, determining the significance parameter of any beam in the beam total set at any moment according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. And S303, determining whether the beam is a singular beam according to the correlation measurement parameter and the significance parameter between the beam corresponding to at least one moment and the adjacent beam. The specific embodiment of S301 is the same as the specific embodiment of S201, and will not be described here again.

Alternatively, as an embodiment, the execution sequence of S301 and S302 is not limited. As another embodiment, S302 is performed after determining that the beam is a candidate beam based on the correlation metric parameter in S301, and at this time, S301 is performed first and S302 is performed next.

In S302, the saliency parameter of the beam is used to check whether the local numerical space autocorrelation is non-random salient. The embodiment of the application does not limit the specific implementation of determining the saliency parameter, for example, the saliency parameter is determined based on the saliency test of the correlation coefficient, the saliency parameter is determined based on the statistic, and the like.

It should be noted that S302 may be performed once for each beam in the beam total set at one time, and S302 may be performed multiple times for each time (the number of times of execution may be the same as the number of beams in the beam total set).

In S303, as one embodiment, the singular beams in the beam set may be determined based on the correlation metric parameter and the saliency parameter corresponding to one time, for example, the correlation metric parameter and the saliency parameter corresponding to the beam are determined based on the measured beam characteristic parameter corresponding to one time, and further, whether the beam is a singular beam is determined, or as another embodiment, the singular beams in the beam set may be determined based on the correlation metric parameter and the saliency parameter corresponding to a plurality of time, for example, the correlation metric parameter and the saliency parameter corresponding to the beam are determined based on the measured beam characteristic parameter corresponding to a plurality of time, and statistics is performed on the correlation metric parameter and the saliency parameter, and further, whether the beam is a singular beam is determined.

The following description of the specific embodiment of S303 illustrates that it may be determined whether the beam is a singular beam by determining whether the beam satisfies any of the following conditions, where if the beam satisfies any of the following conditions, the beam is characterized as a singular beam:

And under the condition six, the correlation measurement parameter is smaller than the first threshold value, and the significance parameter is smaller than the fourth threshold value, wherein the correlation threshold value comprises the first threshold value and the fourth threshold value.

Specifically, a correlation metric parameter less than the first threshold may be considered less relevant to the adjacent beam, while a significance parameter less than the fourth threshold may be considered less relevant to the adjacent beam with a higher confidence, and thus may be determined as a singular beam. The specific values of the first threshold and the third threshold can be adjusted according to actual conditions, and the specific values of the first threshold and the third threshold are not associated.

Optionally, when determining the singular beams in the beam total set based on the correlation metric parameter and the saliency parameter corresponding to one time, if one beam in the beam total set at the time satisfies the condition six, the beam may be determined to be the singular beam, and when determining the singular beams in the beam total set based on the correlation metric parameter and the saliency parameter corresponding to a plurality of times, if one beam in the beam total set at any time satisfies the condition six, the beam may be determined to be the singular beam.

The number of times the beam is determined as the first candidate singular beam according to condition six based on the plurality of measured beam characteristic parameters is greater than a fifth threshold, wherein the correlation threshold comprises the fifth threshold.

Specifically, the first candidate singular beam refers to that, based on the measured beam characteristic parameter of the beam at a certain time, the beam can be determined as a singular beam according to the condition six, and then the beam can be regarded as the first candidate singular beam at the certain time. That is, the correlation metric parameter and the significance parameter of the beam may be determined according to the corresponding measured beam characteristic parameter for any time, and then whether the beam is a singular beam at the time may be determined according to the condition six, the correlation metric parameter, and the significance parameter, and if the beam is a singular beam at the time, it is determined as the first candidate beam.

Counting the number of times that the measured beam characteristic parameters corresponding to the plurality of times respectively are determined as the first candidate singular beam according to the condition six, wherein the number of times is larger than the fifth threshold value can be regarded that the correlation between the beam and the adjacent beam is smaller, and therefore the beam can be determined as the singular beam. The specific value of the fifth threshold can be adjusted according to actual conditions.

In the above scheme, the saliency parameter can be used for checking whether the local numerical space autocorrelation is nonrandom salient, so that the coincidence of the singular beams determined based on the correlation measurement parameter can be reduced by determining the saliency parameter of the beams, and the accuracy of the determined singular beams can be further improved.

A third embodiment of determining the singular beam is described, that is, determining the singular beam by using the correlation metric parameter and the beam classification result, where S102 may include S401, for any beam in the beam set at any moment, determining the correlation metric parameter between the beam and the adjacent beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S402, determining a beam classification result of the beam according to the correlation measurement parameter of the beam and the correlation measurement parameters of other beams in the beam total set at any moment. S403, determining whether the beam is a singular beam according to the beam classification result and the correlation measurement parameter of the beam.

The specific embodiment of S401 is the same as the specific embodiment of S201, and will not be described here again. In S402, the classification category corresponding to the beam classification result is not specifically limited, for example, the beam classification result may include a singular beam and a non-singular beam, or may include a severe singular beam, a moderate singular beam, a mild singular beam, and the like.

In addition, the embodiment of the present application is not particularly limited to a specific implementation manner of determining the beam classification result. Alternatively, the mean and variance may be calculated based on the correlation metric parameters, and the beam classification result may be determined by comparing the magnitude relation between the correlation metric parameters of the beam and the mean and variance, or the distribution of the correlation metric parameters may be determined, and the beam classification result may be determined based on the correlation metric parameters of the beam and the distribution.

S402 may be executed once for each time.

The following description of the specific embodiment of S403 illustrates that it may be determined whether the beam is a singular beam by determining whether the beam satisfies any of the following conditions, where if the beam satisfies any of the following conditions, the beam is characterized as a singular beam:

the beam classification result characterizes the beam as a singular beam.

And the second condition is that the correlation measurement parameter is smaller than a first threshold value at any moment, wherein the correlation threshold value is the first threshold value.

When the singular beam in the beam set is determined based on the correlation metric parameters corresponding to one time and the beam classification result, if one beam in the beam set satisfies the second condition, the beam may be determined as the singular beam, and when the singular beam in the beam set is determined based on the correlation metric parameters corresponding to a plurality of times and the beam classification result, if one beam in the beam set satisfies the second condition, the beam may be determined as the singular beam.

And thirdly, determining that the number of times of the beam determined to be the first candidate singular beam according to the first condition or the second condition in a plurality of moments is larger than a second threshold, wherein the correlation threshold comprises the second threshold.

And a fourth condition, wherein the sum of the number of times weight determined based on the beam classification result and the number of times the beam is determined as a second candidate singular beam according to the third condition is larger than a third threshold, and the correlation threshold comprises the third threshold.

Specifically, the second candidate singular beam means that the beam can be determined as the first candidate beam according to the condition one or the condition two, and the beam can be regarded as the second candidate beam. That is, for any time, it may be determined whether the beam is a singular beam at that time according to the corresponding measured beam characteristic parameter, and if the beam is a singular beam at that time, it is determined as a second candidate beam.

Counting the times of determining the measured beam characteristic parameters corresponding to the plurality of moments as the third candidate singular beam according to the condition III, and configuring the corresponding times for the beam based on the beam classification result corresponding to the beam, wherein the sum of the two times is larger than a third threshold value, and the correlation between the beam and the adjacent beam can be considered to be smaller, so that the beam can be determined as the singular beam. The specific value of the third threshold can be adjusted according to actual conditions.

In the above scheme, after the first candidate singular beam is determined based on the correlation measurement parameter for any time, the number of times of determining the first candidate singular beam in a plurality of times can be counted, so that the sporadic performance is reduced, and the accuracy of the determined singular beam is further improved. In addition, by classifying the singular beams, the mode of determining the singular beams can be further optimized according to the user requirements, so that the suitability of the determined singular beams is improved.

A fourth embodiment of determining the singular beam is described below, where the singular beam is determined using the correlation metric parameter, the significance parameter, and the beam classification result, S102 may include:

S501, determining a correlation measurement parameter between a beam and an adjacent beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam aiming at any beam in the beam total set at any moment. S502, for any beam in the beam total set at any moment, determining the significance parameter of the beam according to the measured beam characteristic parameter of the beam and the measured beam characteristic parameter of the adjacent beam corresponding to the beam. S503, determining the beam classification result of the beam according to the correlation measurement parameter of the beam and the correlation measurement parameters of other beams in the beam total set at any moment. S504, determining whether the beam is a singular beam according to the beam classification result, the correlation measurement parameter and the significance parameter of the beam.

The embodiment of S501 is the same as the embodiment of S201, the embodiment of S502 is the same as the embodiment of S302, and the embodiment of S503 is the same as the embodiment of S402, and will not be repeated here.

The following description of the specific embodiment of S504 illustrates that it may be determined whether the beam is a singular beam by determining whether the beam satisfies any of the following conditions, where if the beam satisfies any of the following conditions, the beam is characterized as a singular beam:

And fifthly, the beam classification result represents that the beam is a singular beam.

And under the condition six, for any moment, the correlation measurement parameter is smaller than a first threshold value, and the significance parameter is smaller than a fourth threshold value, wherein the correlation threshold value comprises the first threshold value and the fourth threshold value.

When the singular beam in the beam set is determined based on the correlation metric parameter, the significance parameter, and the beam classification result corresponding to one time, if one of the beams in the beam set satisfies the condition six at the time, the beam may be determined to be the singular beam, and when the singular beam in the beam set is determined based on the correlation metric parameter, the significance parameter, and the beam classification result corresponding to a plurality of times, if one of the beams in the beam set satisfies the condition six at any time, the beam may be determined to be the singular beam.

And a seventh condition that the number of times the beam is determined to be the first candidate singular beam in a plurality of time instants according to the fifth condition or the sixth condition is greater than a fifth threshold, wherein the correlation threshold comprises the fifth threshold.

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

Specifically, the second candidate singular beam means that a beam can be determined as the first candidate beam according to the condition five or the condition six, and the beam can be regarded as the second candidate beam. That is, for any time, it may be determined whether the beam is a singular beam at that time according to the corresponding measured beam characteristic parameter, and if the beam is a singular beam at that time, it is determined as a second candidate beam.

Counting the times of determining the measured beam characteristic parameters corresponding to the plurality of moments as the third candidate singular beam according to the condition seven, and configuring the corresponding times for the beam based on the beam classification result corresponding to the beam, wherein the sum of the two times is larger than a third threshold value, and the correlation between the beam and the adjacent beam can be considered to be smaller, so that the beam can be determined as the singular beam. The specific value of the third threshold can be adjusted according to actual conditions.

In the above scheme, after the first candidate singular beam is determined based on the correlation measurement parameter and the significance parameter for any time, the number of times of determining the first candidate singular beam in a plurality of times can be counted, so that the sporadic property is reduced, and the accuracy of the determined singular beam is further improved. In addition, by classifying the singular beams, the mode of determining the singular beams can be further optimized according to the user requirements, so that the suitability of the determined singular beams is improved.

Further, based on the above embodiment, the correlation metric parameter may be a local molan index.

In the above scheme, the local morganism index is an important index for measuring the local spatial correlation between the spatial unit and the neighbor thereof in the spatial statistical analysis, wherein the local space is a matrix space in the observation matrix, and the correlation measurement parameter with higher accuracy is obtained by determining the local morganism index corresponding to the wave beam in the matrix space.

A specific implementation of determining the significance parameter in the above embodiment will be described below by taking the correlation metric parameter as a local molan index as an example. That is, S302 or S502 may include performing a loop by randomly selecting one beam from the beam and the adjacent beam as a center beam and the other beams as adjacent beams of the center beam, and calculating a new local Morlan index corresponding to the center beam until a set number of loops is reached. And S602, determining a significance parameter according to the local Morganella index of the beam and the new local Morganella indexes of the cycle times.

In S601, the specific implementation of determining the adjacent beam of the central beam is the same as the specific implementation of determining the adjacent beam of any beam in the above embodiment, and the specific implementation of calculating the new local molan index corresponding to the central beam is also the same as the specific implementation of calculating the local molan index of any beam in the above embodiment, so that the description thereof will not be repeated here.

The specific number of the circulation times can be adjusted according to practical situations, for example, the circulation times can be 999.

In S602, the number of parameters whose absolute value is greater than the absolute value of the local molan index of the beam in the new local molan index of the number of cycles may be counted, and then the quotient of the number of parameters and the number of cycles is determined as the saliency parameter.

In the scheme, by randomly selecting the central beam and calculating the corresponding local Morganella index, whether the correlation between the beam and other beams is low or not can be checked to be a random phenomenon, so that the coincidence of the singular beams determined based on the correlation measurement parameters is reduced, and the accuracy of the determined singular beams is improved.

The following describes a specific implementation manner of determining the beam classification result in the above embodiment. I.e., S402 or S502, may include S701 of acquiring a mean and variance of the correlation metric parameter of the beam and the correlation metric parameters of other singular beams. S702, determining a beam classification result according to the magnitude relation between the correlation measurement parameter of the beam and the mean and variance.

Specifically, the different beam classification results in S702 characterize the different magnitudes of correlation between the beam and the other beams in the beam set. For example, if the beam classification result includes a severe singular beam, a moderate singular beam, and a mild singular beam, the severe singular beam characterizes the beam as having very low correlation with other beams in the beam set, the moderate singular beam characterizes the beam as having low correlation with other beams in the beam set, and the mild singular beam characterizes the beam as having slightly low correlation with other beams in the beam set. Different classifications can be given different times weights, and the more the classification is close to the singular beam, the larger the times weights are.

One embodiment of determining the beam classification result based on the magnitude relationship between the correlation metric parameter and the mean and variance is described below by way of example. Referring to table 1, table 1 shows beam classification results, wherein,A correlation metric parameter representing the beam,Representing the mean of the correlation metric of the beam and the correlation metrics of other singular beams,Representing the variance of the correlation metric for that beam and the correlation metrics for other singular beams.

TABLE 1 Beam classification results

Further, for the first to eighth conditions in the above embodiment, the three beam classification results (serious singular beam, medium singular beam and mild singular beam) are each characterized in that the beam is a singular beam, so that the beam can be determined to be a singular beam, in this case, if the singular beam is not directly determined based on the beam classification results, the beam is not a singular beam, in addition to the beam classification results, in other classification examples, it is necessary to further judge whether the correlation metric parameter of the beam is smaller than a first threshold value, so as to determine whether the beam is a singular beam, in addition to the beam classification results, in the other classification examples, it is necessary to further judge whether the number of times the beam is determined to be a first singular beam or not is larger than a second threshold value, so as to determine whether the beam is a singular beam, in addition to the beam classification results, in the other classification examples, it is necessary to further judge whether the number of times the beam is determined to be a first singular beam, in the case of which the number of times is larger than a second singular beam is larger than a second threshold value, in the fourth condition, so that the sum of the number of times the beam is determined to be a second singular beam is larger than a third threshold value, in the case of times of the beam is determined to be a singular beam is larger than a singular beam, in the case of the number of times is further judged to be a second candidate is determined to be a singular beam, in the case of the number of times is larger than a first threshold value, in the case of times is further judging that the number is necessary to be a first number is larger than a first candidate is larger than a singular beam is larger than a first threshold value, so as is larger than a first threshold value, whether the number is necessary to be a first threshold value is further judged to be a, thereby determining whether the beam is a singular beam.

In the above scheme, the singular beams can be classified based on the mean value and the variance of the correlation measurement parameters, and as the mean value and the variance of the correlation measurement parameters reflect the distribution condition of the correlation measurement parameters of each beam, the beams with extremely low correlation and slightly low correlation can be distinguished, thereby improving the accuracy of the determined singular beams.

The following describes a specific embodiment of determining adjacent beams corresponding to the beams. That is, before S102, the method for reconstructing beam characteristic parameters provided by the embodiment of the present application may further include S801, for any beam in the beam set, determining, based on the set sliding window, a measured beam characteristic parameter of a neighbor beam corresponding to the beam from the observation matrix. And S802, determining the measurement beam characteristic parameters of the adjacent beam from the measurement beam characteristic parameters of the adjacent beam based on the set adjacent indication function.

In S801, the observation matrix includes measured beam characteristic parameters of each beam in the beam set, and the sliding window is used to determine measured beam characteristic parameters of neighbor beams corresponding to any one beam from the above observation matrix. Any beam can be used as a central beam, and other beams in the sliding window can be used as neighbor beams corresponding to the beam.

As one embodiment, a sliding windowA3 x 3 matrix implementation may be employed, namely:

。

in S802, the adjacency indication function is used to determine whether or not the neighboring beam corresponding to the beam belongs to the adjacent beam corresponding to the beam. As one embodiment, the adjacency rule may be set to satisfy the following condition:

;

wherein, the Representing the positionI.e. up, down, left, right and diagonal 8-directional neighbors.

The adjacency indicating function can be expressed as:

;

wherein, the Representing an adjacency indicating function for determining positionWhether or not it isIs a neighbor of (c).

Alternatively, based on the adjacency indication function in the above example, neighbor beams in the directions of up, down, left, right and diagonal directions corresponding to the beams are determined as adjacency beams, and in other cases, only part of the neighbor beams in the directions of 8 may be determined as adjacency beams.

In the above scheme, the neighbor beam is determined from the observation matrix based on the set sliding window, and the neighbor beam is determined from the neighbor beams based on the set neighbor indication function, so that the neighbor beam of the beam can be quickly found from the beam total set, and further the correlation measurement parameter corresponding to the beam is conveniently determined.

Optionally, on the basis of the foregoing embodiment, for the beam located at the edge of the observation matrix, the adjacent beam includes a complementary beam and a beam located in the sliding window in the observation matrix, and the measurement beam characteristic parameter of the complementary beam is an average value of measurement beam characteristic parameters of the neighbor beams in the sliding window.

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

Referring to fig. 2, fig. 2 is a schematic diagram of an observation matrix provided in an embodiment of the present application, in which a solid circle in fig. 2 represents a supplementary beam, a hollow circle represents a beam in a total set of beams, a central numeral thereof represents a beam identifier corresponding to the beam, a dashed box represents a sliding window, and an arrow represents a sliding direction.

Taking the observation matrix shown in fig. 2 as an example, meanwhile, assume that the measured beam characteristic parameter is RSRP and the correlation metric parameter is a local moland index, a specific calculation process of a method for reconstructing the beam characteristic parameter provided by the embodiment of the present application is described below.

Full set of beamsCorresponding observation matrixCan be expressed as:

;

wherein, the Representation ofMedium beam identification asIs located within a matrixRow of linesThe number of columns in a row,Representing the number of rows of the observation matrix,The number of columns of the observation matrix is shown.

Based on sliding windowsFor the above observation matrixAny position in (3)Extracted bySub-matrix for central beamSub-matrixIncluding the central beamIs set to the RSRP of (and the center beam)RSRP of the corresponding neighbor beam.

Adjacency indication function based on settingDetermining a center beam from the RSRP of the neighbor beamsWherein the central beam is the RSRP of the adjacent beam of (a)The RSRP of (a) and its neighboring beams can be expressed as:

。

The local molan index can be calculated using the following formula:

;

wherein, the Representing the positionThe local molan index corresponding to the beam of (c),Indicating the RSRP to which the beam corresponds,Mean value of RSRP representing the beam and RSRP of adjacent beam corresponding to the beam:

;

representing the RSRP of the beam and the variance of the RSRP of the adjacent beam corresponding to the beam:

;

Representation of In (a)Weighting of the location.

For the beam with the local Morganella index smaller than 0, to verify the local Morganella indexA non-parametric permutation test method based on a computational window may be employed. In the significance test, the center point is used forTogether with all values in its neighborhood, form a set to be permutedAnd randomly selecting one value as a center and the rest as a neighborhood in each permutation, thereby calculating the local Morganella index under the permutation:

;

wherein, the After the representation of the substitutionMiddle positionThe RSRP of the beam at the point,Represent the firstSub-circulation andThe range of the values is as follows,Indicating the number of cycles.

Then, the original local Morganella index is calculated by the indication functionExtreme statistics during substitution-empirical P value:

;

wherein, the Indicating a function, the value is 1 when the condition is satisfied, otherwise, 0.

The final significance result can be expressed as:

;

wherein, the Is a significance level threshold (typically 0.05) and if the result is 1, the local molan index for that location is significant, at which point the beam can be considered the first candidate singular beam.

Definition setAdding the beam identification corresponding to the beam judged by the significance to the setIn (a):

;

Statistics Sets respectively corresponding to a plurality of momentsObtaining a plurality of setsThe number of times each beam identification occurs is counted. If the number of times a certain beam corresponds is greater than the third threshold, the beam can be considered as a second candidate singular beam.

Set the set of partial Morganella indexes of singular beams asThe definition is as follows:

;

calculating the mean value of the local Morgan indexes corresponding to all singular beams And variance of:

;

Wherein, the Is a collectionIs a sum of elements of (a) and (b).

The classification is shown in Table 1, and when the beam is judged to be a serious singular beam, the weight is given to the number of the beams5, Which corresponds to five times the beam was decided as a singular beam. And so on, finally determining the number of times weight of the beamNumber of times the beam is determined to be a second candidate singular beamAnd determining the beam as a singular beam when the sum is greater than a sixth threshold.

Referring to fig. 3, fig. 3 is a flowchart of a method for reconstructing beam characteristic parameters applied to a ue according to an embodiment of the present application, where a reconstruction model is deployed on the ue. The method for reconstructing beam characteristic parameters may include receiving reference signals of each beam in a subset of beams transmitted by a network device S901. And S902, measuring the measured beam characteristic parameters of each beam in the beam subset based on the reference signals. S903, reconstructing predicted beam characteristic parameters of other beams except the beam subset in the beam total set based on the measured beam characteristic parameters of each beam in the beam subset.

The network device may store the beam identities of the beams in the beam subset, and the network device may send the reference signals of the beams in the beam subset to the user terminal, where the beam subset includes singular beams having correlations with other beams below a correlation threshold. After receiving the reference signals of each beam in the beam subset sent by the network equipment, the user terminal obtains the measured beam characteristic parameters of each beam in the beam subset based on the reference signals.

In S903, the terminal may reconstruct predicted beam characteristic parameters of the other beams in the beam subset except for the beam subset based on the measured beam characteristic parameters of the respective beams in the beam subset. Specifically, the measured beam characteristic parameters of each beam in the beam subset may be input into the reconstruction model, so as to obtain the predicted beam characteristic parameters of other beams in the beam subset output by the reconstruction model. Wherein in some cases the output of the reconstruction model may include predicted beam characteristic parameters of the subset of beams in addition to predicted beam characteristic parameters of other beams in the total set of beams than the subset of beams.

In the above scheme, when the predicted beam characteristic parameters of the other beams except the beam subset in the beam subset are reconstructed through the measured beam characteristic parameters of each beam in the beam subset, singular beams with low correlation with the other beams in the beam subset can be added, so that even if the beam characteristic parameters corresponding to the singular beams cannot be obtained through reconstruction, the corresponding beam characteristic parameters can be obtained through measurement. Therefore, the method provided by the embodiment of the application can obtain the beam characteristic parameters of the beams with higher correlation through reconstruction, and can also obtain the beam characteristic parameters of the beams with lower correlation through measurement, thereby improving the accuracy of the prediction result of the beam corpus obtained through reconstruction.

Optionally, on the basis of the foregoing embodiment, the method for reconstructing beam characteristic parameters according to the embodiment of the present application may further include a step of determining a singular beam. As an embodiment, after the user terminal establishes a connection with the network device, the step of determining the singular beam may be performed, and the step of reconstructing the beam characteristic parameter may be performed when data transmission is required, where the step of determining the singular beam is performed before the step of reconstructing the beam characteristic parameter.

As another embodiment, the step of determining the singular beam may be performed periodically or may be performed when needed (e.g., when a large change in the communication environment occurs), and at this time, the step of determining the singular beam may be performed before or after the step of reconstructing the beam characteristic parameters. If the step of determining the singular beam is performed before the step of reconstructing the beam characteristic parameters, the step of reconstructing the beam characteristic parameters may be performed based on the newly determined singular beam, and if the step of determining the singular beam is performed after the step of reconstructing the beam characteristic parameters, the next step of reconstructing the beam characteristic parameters may be performed based on the newly determined singular beam.

The method for reconstructing the beam characteristic parameters provided by the embodiment of the application can further comprise S1001, receiving the reference signals of each beam in the beam total set sent by the network equipment at different moments. S1002, for any moment, measuring the measured beam characteristic parameters of each beam in the beam total set based on the reference signal transmitted at the moment. S1003, determining singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment. And S1004, transmitting beam information corresponding to the singular beams to the network equipment.

The specific embodiments of S1001 and S1002 are the same as the specific embodiment of S101, and the specific embodiment of S1003 is the same as the specific embodiment of S102, and will not be repeated here.

In S1004, the form of the beam information is not specifically limited, and for example, the beam information may include a beam identifier of a singular beam, the number of times the singular beam is determined as a candidate singular beam, a correlation metric of the singular beam, and the like.

As an implementation manner, the terminal may send beam information corresponding to the singular beam to the network device through signaling. For example, two variables, reportquality=cri-RSRP-Odd and numberOfOddBeams =m, can be added to existing signaling, where reportquality=cri-RSRP-Odd is used to transmit the beam identity corresponding to the singular beam and the number of times the singular beam is determined as a candidate singular beam, numberOfOddBeams =m is used to transmit the number of singular beams.

In the above scheme, the beam reconstruction process is a process of reconstructing the predicted beam characteristics of the beam whole set through the measured beam characteristic parameters of the beam subset, so that the correlation between the beam and other beams in the beam whole set can be determined based on the measured beam characteristic parameters of the beam, and further, the singular beam with higher accuracy can be determined.

Further, on the basis of the foregoing embodiment, S903 may include inputting the measured beam characteristic parameters of each beam in the beam subset into a pre-trained reconstruction model to obtain predicted beam characteristic parameters, where the arrangement order of each beam in the observation matrix is consistent with the arrangement order of each beam in the beam matrix when the reconstruction model is trained.

The embodiments corresponding to the observation matrix have been described in detail in the foregoing embodiments, and are not described herein again.

In the above-mentioned scheme, since the method provided by the embodiment of the present application aims to find the beams which may not be obtained due to the low correlation in the reconstruction process from the total set of beams, by setting the arrangement sequence of each beam in the observation matrix to be consistent with the arrangement sequence of each beam in the beam matrix when the reconstruction model is trained, the adjacent beams of the beams in the two processes are consistent, thereby improving the accuracy of the determined singular beams.

Further, on the basis of the above embodiment, a singular beam decider (for determining singular beams) and a molan index calculator (for calculating local molan index) may be disposed on the user terminal, and an optimization pattern generator (for updating the beam subset) may be disposed on the network device.

Referring to fig. 4, fig. 4 is an interaction diagram of a first method for determining a beam according to an embodiment of the present application, where the method for determining a beam may include S1101 that a network device sends reference signals of each beam in a beam set to a user terminal at different times. The user terminal receives the reference signals of the beams in the beam total set transmitted by the network device at different times S1102. S1103, for any moment, the ue measures the measured beam characteristic parameters of each beam in the beam set based on the reference signal transmitted at the moment. And S1104, the user terminal determines singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment. And S1105, the user terminal sends beam information corresponding to the singular beams to the network equipment. And 1106, the network equipment receives beam information corresponding to the singular beam sent by the user terminal. And S1107, the network equipment updates the beam subset based on the beam information corresponding to the singular beams. The network device sends reference signals for each beam in the subset of beams to the user terminal S1108. S1109, the ue receives the reference signals of each beam in the beam subset sent by the network device. The user terminal measures the measured beam characteristic parameters of each beam in the beam subset based on the reference signal S1110. And S1111, the user terminal reconstructs the predicted beam characteristic parameters of the other beams except the beam subset in the beam total set based on the measured beam characteristic parameters of each beam in the beam subset.

Referring to fig. 5, fig. 5 is a flowchart of a method for reconstructing beam characteristic parameters applied to a network device according to an embodiment of the present application, where a reconstruction model is deployed on the network device. The method of reconstructing beam characteristic parameters may include S1201 transmitting reference signals for each beam in a subset of beams to a user terminal. S1202, receiving the measured beam characteristic parameters of each beam in the beam subset sent by the user terminal. S1203 reconstruct predicted beam characteristic parameters of the other beams in the beam subset except the beam subset based on the measured beam characteristic parameters of each beam in the beam subset.

Specifically, the network device stores the beam identifier of each beam in the beam subset, and the network device may send the reference signal of each beam in the beam subset to the user terminal, where the beam subset includes singular beams with correlation with other beams lower than a correlation threshold. After receiving the reference signals of each beam in the beam subset sent by the network equipment, the user terminal obtains the measured beam characteristic parameters of each beam in the beam subset based on the reference signals, and sends the measured beam characteristic parameters of each beam in the beam subset to the network equipment.

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

Optionally, on the basis of the foregoing embodiment, before S1201, the method for reconstructing beam characteristic parameters according to the embodiment of the present application may further include updating the beam subset based on beam information corresponding to the singular beam.

In particular, as one implementation, the network device may update the beam subset immediately after receiving the beam information corresponding to the singular beam, or as another implementation, the network device may update the beam subset not immediately after receiving the beam information corresponding to the singular beam, but before needing to send the beam subset to the user terminal.

The embodiment of the present application does not specifically limit the specific implementation of updating the beam subset. For example, when there are no singular beams in the original beam subset, a singular beam may be added to the beam subset, or a beam that was last determined to be added to the beam subset is not determined to be a singular beam this time, the beam may be deleted from the beam subset, and so on.

In the above scheme, after receiving the beam information of the singular beams sent by the user terminal, the network device may update the beam subset according to the beam information of the singular beams instead of adding all the singular beams into the beam subset, thereby improving the flexibility of updating the beam subset.

The step of updating the beam subset based on the beam information corresponding to the singular beam may include adding the beam identifier of the singular beam to the beam subset if the number of beam occurrences is greater than a seventh threshold.

The number of beam occurrences characterizing the number of singular beams determined to be candidate singular beams, which number is greater than a seventh threshold, may be considered to be less correlated with other beams, and therefore the beam identity of the singular beam may be added to the subset of beams.

It should be noted that, the specific value of the seventh threshold may be adjusted according to the actual situation. As one embodiment, the seventh threshold may be determined based on communication environment information including at least one of user speed, channel state change, multipath effects, whether occlusion occurs.

In the above scheme, for the beam determined to be the singular beam, whether the beam identifier corresponding to the beam is added to the beam subset can be further determined based on the size of the beam, so that the flexibility of increasing the singular beam is improved.

Further, on the basis of the foregoing embodiment, the method for reconstructing beam characteristic parameters according to the embodiment of the present application may further include a step of determining a singular beam. As an embodiment, after the user terminal establishes a connection with the network device, the step of determining the singular beam may be performed, and the step of reconstructing the beam characteristic parameter may be performed when data transmission is required, where the step of determining the singular beam is performed before the step of reconstructing the beam characteristic parameter.

The method for reconstructing the beam characteristic parameters provided by the embodiment of the application can further comprise S1301, wherein the reference signals of the beams in the beam total set are sent to the user terminal at different moments. And S1302, for any moment, receiving the measured beam characteristic parameters of each beam in the beam total set obtained by the user terminal based on the reference signal transmitted at the moment. S1303, determining singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment.

The specific embodiments of S1301 and S1302 are the same as the specific embodiment of S101, and the specific embodiment of S1303 is the same as the specific embodiment of S102, and will not be repeated here.

Referring to fig. 6, fig. 6 is an interaction diagram of a second method for determining a beam according to an embodiment of the present application, where the method for determining a beam may include S1401 that a network device sends reference signals of each beam in a beam set to a user terminal at different times. S1402 the ue receives reference signals of each beam in the beam total set transmitted by the network device at different times. S1403, for any time, the ue measures the measured beam characteristic parameters of each beam in the beam set based on the reference signal transmitted at that time. And S1404, the user terminal sends the measured beam characteristic parameters of each beam in the beam subset corresponding to the moment to the network equipment. And S1405, the network equipment receives the measured beam characteristic parameters of each beam in the beam subset corresponding to the moment sent by the user terminal. And S1406, the network equipment determines singular beams in the beam total set according to the measured beam characteristic parameters corresponding to at least one moment. And 1407, the network equipment 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 subset of beams to the user terminal. S1409 the user terminal receives the reference signals for each beam in the subset of beams sent by the network device. The user terminal measures the measured beam characteristic parameters of each beam in the beam subset based on the reference signal S1410. The user terminal sends the measured beam characteristic parameters of each beam in the subset of beams to the network device S1411. The network device receives the measured beam characteristic parameters of each beam in the beam subset sent by the user terminal S1412. The network device reconstructs predicted beam characteristic parameters for the other beams in the beam subset except for the beam subset based on the measured beam characteristic parameters for each beam in the beam subset S1413.

Referring to fig. 7, fig. 7 is a block diagram of an electronic device 1500 according to an embodiment of the present application, where the electronic device 1500 includes at least one processor 1501, at least one communication interface 1502, at least one memory 1503 and at least one communication bus 1504. Where communication bus 1504 is used to enable direct connected communication of these components, communication interface 1502 is used to communicate signaling or data with other node devices, and memory 1503 stores machine readable instructions executable by processor 1501. When the electronic device 1500 is in operation, the processor 1501 and the memory 1503 communicate over the communication bus 1504, and machine-readable instructions when invoked by the processor 1501 perform the above-described methods.

As an embodiment, the electronic device 1500 may be a user terminal, and different user terminals may be connected to each other by a wired or wireless manner. User terminals may be widely used in various scenarios, such as near field communication (NEAR FIELD Communications), device-to-Device (D2D), vehicle-to-Device (Vehicle to Everything, V2X) communication, machine-type communication (Machine-type Communication, MTC), internet of things (Interne to Things, IOT), virtual reality, augmented reality, industrial control, autopilot, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc.

The user Terminal may also be called a Mobile Station (MS), a Terminal (Terminal), a Terminal device (Terminal Equipment), a Subscriber Unit (subscnriber Unit), a Cellular Phone (Cellular Phone), a smart Phone (Smar Phone), a wireless data card, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA) Computer, a tablet, a wireless Modem (Modem) handset (Handheld), a Laptop (lapop Computer), a Cordless Phone (Cordless Phone) or a wireless local loop (Wireless Localloop, WLL) Station, a machine type communication (MACHINE TYPEC Comunication, MTC) Terminal, etc. For convenience of description, in all embodiments of the present application, the above-mentioned devices are collectively referred to as a user terminal.

The aforementioned user terminal may further comprise an antenna and a transceiver. The transceiver conditions (e.g., analog converts, filters, amplifies, and upconverts, etc.) the output samples and generates an uplink signal, which is transmitted via an antenna to the network device, and on the downlink, the antenna receives the downlink signal transmitted by the network device, and the transceiver conditions (e.g., filters, amplifies, downconverts, digitizes, etc.) the received signal from the antenna and provides input samples. The processor 1501 is configured to perform the method of reconstructing beam characteristic parameters or the method of determining a beam described in the above embodiments. The embodiment of the application does not limit the specific technology and the specific equipment form adopted by the user terminal.

As another embodiment, the electronic device 1500 may be a network device, and the user terminal may be connected to the network device in a wireless manner. The network device may also interface with or transmit information to and from an evolved universal terrestrial Radio access (Evolved Universal Terrestrial Radio Access, E-UTRA) system, a New Radio (NR) system, and a future Radio access system defined in the third generation partnership project (3rd Generation Partnership Project,3GPP). The network device may also be connected to devices in a radio access system comprising two or more different types of the above. An open Radio access network (Pen Radio ACCESS NET, O-RAN) may also be connected to the network device.

The network device may be configured with a module for implementing the base station functionality. The means for implementing the Base Station function may implement the functions of a Base Station (Base Station), an evolved Base Station (Rvolved NodeB, eNodeB or eNB), a transmission and reception point (Transmission Reception Point, TRP), a Next Generation Base Station (gNB) in a fifth Generation (5th Generation,5G) mobile communication system, a Next Generation Base Station in a sixth Generation (6th Generation,6G) mobile communication system, a Base Station in a future mobile communication system, or an access node in a WiFi system.

The base station may further comprise an antenna and a transceiver. On the uplink, uplink signals from the user terminals are received via the antennas, mediated by the transceivers and further processed by the processor 1501 to recover signaling information sent by the user terminals, and on the downlink, signaling messages are processed by the processor 1501, mediated by the transceivers to generate downlink signals, which are transmitted via the antennas to the user terminals. The processor 1501 is also configured to perform the method of reconstructing beam characteristic parameters or the method of determining a beam 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 be a relay node or a donor node.

It will be appreciated that the above description is merely illustrative of a simplified design of a base station, and that in practice a base station may include any number of transmitters, receivers, processors, controllers, memories, communication units, etc., and all base stations that may implement the present application are within the scope of the present application.

The processor 1501 includes one or more, which may be an integrated circuit chip, having signal processing capabilities. The Processor 1501 may be a general-purpose Processor including a central processing unit (Central Processing Unit, CPU), a micro control unit (Micro Controller Unit, MCU), a network Processor (Network Processor, NP) or other conventional Processor, or a special-purpose Processor including a neural network Processor (Neural-network Processing Unit, NPU), a graphics Processor (Graphics Processing Unit, GPU), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application SPECIFIC INTEGRATED Circuits (ASIC), a field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. Also, when the processor 1501 is plural, some of them may be general-purpose processors, and another may be special-purpose processors.

Memory 1503 includes one or more of, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for reconstructing beam characteristic parameters, characterized in that it is applied to a user terminal, and the method comprises:

receiving a reference signal of each beam in a beam subset sent by a network device, wherein the beam subset includes a singular beam, and a correlation between the singular beam and other beams in the full beam set in which the beam subset is located is lower than a correlation threshold;

Measuring a measurement beam characteristic parameter of each beam in the beam subset based on the reference signal;

The predicted beam characteristic parameters of the beams other than the beam subset in the entire beam set are reconstructed based on the measured beam characteristic parameters of each beam in the beam subset.

2. The method according to claim 1, characterized in that the method further comprises:

receiving a reference signal of each beam in the entire beam set sent by the network device at different times;

At any moment, measuring a measurement beam characteristic parameter of each beam in the entire beam set based on the reference signal sent at the moment;

Determine the singular beam in the entire beam set according to the measurement beam characteristic parameter corresponding to at least one moment;

Send beam information corresponding to the singular beam to the network device.

3. The method according to claim 2, characterized in that the determining the singular beam in the entire beam set according to the characteristic parameters of the measurement beam corresponding to at least one moment comprises:

For any beam in the full beam set at any moment, determine a correlation measurement parameter between the beam and the adjacent beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam;

Whether the beam is the singular beam is determined according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment.

4. The method according to claim 3, characterized in that the determining the singular beam in the entire beam set according to the characteristic parameters of the measured beam corresponding to at least one moment further comprises:

For any beam in the entire beam set at any moment, determine a significance parameter of the beam according to the measurement beam characteristic parameter of the beam and the measurement beam characteristic parameter of the adjacent beam corresponding to the beam;

Correspondingly, determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment includes:

Determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment and the significance parameter;

or,

The determining whether the beam is the singular beam according to a correlation measurement parameter between the beam and the adjacent beam corresponding to at least one moment includes:

At any moment, determining a beam classification result of the beam according to the correlation metric parameter of the beam and the correlation metric parameters of other beams in the entire beam set;

Determine whether the beam is the singular beam according to the beam classification result and the correlation measurement parameter of the beam.

5. A method for reconstructing beam characteristic parameters, characterized in that it is applied to a network device, and the method comprises:

Sending a reference signal of each beam in a beam subset to a user terminal, wherein the beam subset includes a singular beam, and a correlation between the singular beam and other beams in the full beam set in which the beam subset is located is lower than a correlation threshold;

Receiving a measured beam characteristic parameter of each beam in the beam subset sent by the user terminal;

6. The method according to claim 5, characterized in that before sending the reference signal of each beam in the beam subset to the user terminal, the method further comprises:

The beam subset is updated based on beam information corresponding to the singular beam.

7. The method according to claim 5 or 6, characterized in that the method further comprises:

Sending, at different times, a reference signal of each beam in the entire beam set to the user terminal;

At any moment, receiving a measured beam characteristic parameter of each beam in the entire beam set obtained by the user terminal based on the reference signal sent at the moment;

The singular beam in the entire beam set is determined according to the measurement beam characteristic parameter corresponding to at least one moment.

8. A method for determining a beam, comprising:

Obtaining a measured beam characteristic parameter obtained by measuring a reference signal of each beam in a full beam set sent by a user terminal at different times based on a network device;

A singular beam in the beam set is determined according to the measurement beam characteristic parameter corresponding to at least one moment, wherein a correlation between the singular beam and other beams in the beam set is lower than a correlation threshold.

9. A method for reconstructing object feature parameters, comprising:

Acquire a measurement object characteristic parameter of each object in the object subset, wherein the object subset includes a singular object, and the correlation between the singular object and other objects in the full object set where the object subset is located is lower than a correlation threshold;

The predicted object feature parameters of other objects in the entire object set except the object subset are reconstructed based on the measured object feature parameters of each object in the object subset.

10. An electronic device, comprising: 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 method according to any one of claims 1 to 9 by calling the computer program instructions.