US-20260128511-A1 - METHOD OF PERFORMING BEAMFORMING AND AN APPARATUS THEREOF

US20260128511A1US 20260128511 A1US20260128511 A1US 20260128511A1US-20260128511-A1

Abstract

Systems, devices, methods, and instructions for performing beamforming on a signal by an electronic apparatus are provided, including training a deep neural network associated with beamforming on a signal, identifying an input signal input through an antenna array element, obtaining an autocorrelation matrix corresponding to the input signal, obtaining a weight vector from the autocorrelation matrix based on the deep neural network, and obtaining an output signal of the antenna array element corresponding to the input signal based on the weight vector.

Inventors

Jae Hyuk Lim
Sun Jin OH
Eui Hyuk LEE
Sung kweon Kim
Dae Kyo JEONG

Assignees

AGENCY FOR DEFENSE DEVELOPMENT

Dates

Publication Date: 20260507
Application Date: 20241219
Priority Date: 20241105

Claims (10)

1 . A method of performing beamforming on a signal by an electronic apparatus, the method comprising: training a deep neural network associated with beamforming on a signal; identifying an input signal input through an antenna array element; obtaining an autocorrelation matrix corresponding to the input signal; obtaining a weight vector from the autocorrelation matrix based on the deep neural network; and obtaining an output signal of the antenna array element corresponding to the input signal based on the weight vector.
2 . The method of claim 1 , wherein training the deep neural network comprises: modeling a training input signal; obtaining a training autocorrelation matrix corresponding to the training input signal; obtaining an input data vector based on the training autocorrelation matrix; and training the deep neural network to output an output data vector comprising a training output weight in response to an input of the input data vector, and the training of the deep neural network comprises feedback according to a loss function defined based on an optimal array element weight vector for a null-space beamforming method and the output data vector.
3 . The method of claim 2 , wherein the deep neural network comprises: an input layer for receiving input data; an output layer for outputting output data; and at least one hidden layer located between the input layer and the output layer, and each hidden layer included in the at least one hidden layer comprises: a fully connected layer; and a rectified linear unit (ReLU) layer.
4 . The method of claim 2 , wherein modeling the training input signal comprises modeling the training input signal based on a uniform linear array comprising isotropic elements.
5 . The method of claim 2 , wherein the optimal array element weight vector for the null-space beamforming method is calculated based on an interference signal space comprising a plurality of steering vectors.
6 . The method of claim 2 , wherein obtaining the input data vector comprises: obtaining an upper triangular matrix element vector based on the training autocorrelation matrix; obtaining a training element vector corresponding to the training autocorrelation matrix based on real and imaginary parts extracted from the upper triangular matrix element vector; and normalizing the training element vector to obtain the input data vector.
7 . The method of claim 6 , wherein the output data vector is obtained based on real and imaginary parts extracted from the training output weight.
8 . The method of claim 2 , wherein training the deep neural network comprises: training the deep neural network to minimize an operational value of the loss function based on a plurality of input data vectors set for the training of the deep neural network including the input data vector and a plurality of output data vectors set for the training of the deep neural network including the output data vector.
9 . A non-transitory computer-readable storage medium having a program for executing on a computer a method of performing beamforming by an electronic apparatus recorded thereon, the method comprising: training a deep neural network associated with beamforming on a signal; identifying an input signal input through an antenna array element; obtaining an autocorrelation matrix corresponding to the input signal; obtaining a weight vector from the autocorrelation matrix based on the deep neural network; and obtaining an output signal of the antenna array element corresponding to the input signal based on the weight vector.
10 . An electronic apparatus for performing beamforming on a signal, the electronic apparatus comprising: a processor, and one or more memory for storing one or more instructions, wherein the one or more instructions, when executed, causes the processor to: train a deep neural network associated with beamforming on a signal; identify an input signal input through an antenna array element; obtain an autocorrelation matrix corresponding to the input signal; obtain a weight vector from the autocorrelation matrix based on the deep neural network; and obtain an output signal of the antenna array element corresponding to the input signal based on the weight vector.

Description

PRIORITY INFORMATION This application claims the benefit of Korean Patent Application No. 10-2024-0155303, filed on Nov. 5, 2024 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. FIELD OF THE DISCLOSURE The present disclosure relates to systems, devices, methods, and instructions for performing beamforming. An example implementation relates to a method of performing beamforming in a large array while minimizing degradation of signal-to-inference-plus-noise ratio (SINR) performance based on a null-space beamforming method by an electronic apparatus and an apparatus thereof. DISCUSSION OF THE RELATED ART Beamforming methods have attempted to directly calculate the weights of antenna array elements based on the minimum variance distortion-less response (MVDR) method without separate processing. However, the problem with the MVDR method is that the signal-to-interference noise ratio (SINR) performance deteriorates as the number of array elements increases. In order to apply the blind beamforming method using a deep neural network in a defense industry system that requires a large number of array elements in the antenna, it is necessary to improve the SINR performance degradation problem that occurs as the number of array elements increases. Therefore, the present disclosure proposes a beamforming method that minimizes the SINR performance degradation in large array elements based on a null-space beamforming method SUMMARY An aspect provides an electronic apparatus capable of performing beamforming in a large array with minimum degradation of signal-to-inference-plus-noise ratio (SINR) performance based on a null-space beamforming method. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings According to an aspect, there is provided a method of performing beamforming on a signal by an electronic apparatus, the method including training a deep neural network associated with beamforming on a signal, identifying an input signal input through an antenna array element, obtaining an autocorrelation matrix corresponding to the input signal, obtaining a weight vector from the autocorrelation matrix based on the deep neural network, and obtaining an output signal of the antenna array element corresponding to the input signal based on the weight vector. In the method of performing beamforming by an electronic apparatus according to an example embodiment, training the deep neural network may include modeling a training input signal, obtaining a training autocorrelation matrix corresponding to the training input signal, obtaining an input data vector based on the training autocorrelation matrix, and training the deep neural network to output an output data vector comprising a training output weight in response to an input of the input data vector, and the training of the deep neural network may include feedback according to a loss function defined based on an optimal array element weight vector for a null-space beamforming method and the output data vector. In the method of performing beamforming by an electronic apparatus according to an example embodiment, the deep neural network may include an input layer for receiving input data, an output layer for outputting output data, and at least one hidden layer located between the input layer and the output layer, and each hidden layer included in the at least one hidden layer may include a fully connected layer, and a rectified linear unit (ReLU) layer. In the method of performing beamforming by an electronic apparatus according to an example embodiment, modeling the training input signal may include modeling the training input signal based on a uniform linear array comprising isotropic elements. In the method of performing beamforming by an electronic apparatus according to an example embodiment, the optimal array element weight for the null-space beamforming method may be calculated based on an interference signal space comprising a plurality of steering vectors. In the method of performing beamforming by an electronic apparatus according to an example embodiment, obtaining the input data vector may include obtaining an upper triangular matrix element vector based on the training autocorrelation matrix, obtaining a training element vector corresponding to the training autocorrelation matrix based on real and imaginary parts extracted from the upper triangular matrix element vector, and normalizing the training element vector to obtain the input data vector. In the method of performing beamforming by an electronic apparatus according to an example embodiment, th