KR101732509B1 - Method for determining beamforming coefficient in wireless communication system - Google Patents

Abstract

A method for determining a beamforming coefficient in a wireless communication system is disclosed. A method of determining a beamforming coefficient in a wireless communication system according to an embodiment of the present invention includes: approximating a variation matrix corresponding to a vector component of a received signal by a circulant matrix; And determining a beamforming coefficient using the approximated scattering matrix.

Description

[0001] METHOD FOR DETERMINING BEAM FORMING COEFFICIENT IN WIRELESS COMMUNICATION SYSTEM [0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to beamforming in a wireless communication system having multiple antennas, and more particularly to a technique for determining a beamforming coefficient.

In the wireless communication system, the technique based on the receiving side antenna beamforming is a main technical element for improving the system capacity, and researches are being actively conducted around the world. Considering such global research trends, effective adaptive beamforming technology is a key factor in maintaining market competitiveness of wireless communication systems.

However, when the number of receiving antennas is large, the adaptive beamforming technique requires a large amount of computational complexity. Techniques using fixed beam patterns instead of adaptive beamforming techniques have been considered as an alternative to this, but this is merely a workaround for adaptive beamforming techniques in terms of performance. As a result, it is difficult to achieve a beamforming technique having good performance while maintaining computational complexity applicable to a commercial wireless communication system. Especially in applications where it is important to reduce power consumption, such as in low-power devices in wireless networks for the Internet of things, adaptive beamforming techniques with lower computational complexity are needed.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a method for determining a beamforming coefficient in a wireless communication system having a receiving multiple antenna for efficient beamforming.

According to an aspect of the present invention, there is provided a method for determining a beamforming coefficient in a wireless communication system, the method including: approximating a variation matrix corresponding to a vector component of a received signal by a circulant matrix; ; And determining a beamforming coefficient using the approximated scattering matrix.

Here, approximating the dispersion matrix to the circulation matrix may approximate all the columns constituting the dispersion matrix to the circulation matrix.

Here, approximating the dispersion matrix to the circulation matrix may be approximated by the circulation matrix after adjusting a time domain factor of the dispersion matrix.

만큼 증가시킨 후에, 상기 순환 행렬로 근사화 할 수 있다.The step of adjusting the time domain factors to approximate the time domain factors to the circulation matrix may include calculating a first row in at least one column of each of the columns of the dispersion matrix

, Then it can be approximated by the recursive matrix.

중에서 시간 컨디셔닝 값

는 1보자 작은 양의 실수일 수 있다. Here,

Among the time conditioning values

Can be a small amount of mistakes.

The step of determining the beamforming coefficient using the approximated scattering matrix may comprise: decomposing the approximated scattering matrix into an inverse discrete Fourier transform matrix, a diagonal matrix, and a discrete Fourier transform matrix; And determining the beamforming coefficient by adjusting an azimuth domain factor of an inverse diagonal matrix with respect to the diagonal matrix.

이라고 할 때, 상기 수학식

의 함수

를 만족하는 방위각 도메인 인자

의 방위각 컨디셔닝 값

을 1보다 작은 양의 실수로 하여 상기 방위각 도메인 인자 B를 조정할 수 있다.Here, if the inverse diagonal matrix is expressed by Equation

, The equation

Function of

≪ / RTI >

Azimuth conditioning value of

The azimuthal domain factor B can be adjusted to be a positive real number smaller than one.

According to the present invention, in the present invention, the beamforming coefficients are determined efficiently by a small amount of computation in the multiple antennas on the reception side, thereby realizing the performance of the wireless communication system with high efficiency while minimizing the power consumption.

1 is a block diagram illustrating one embodiment of a station performing a method in accordance with the present invention.
2 is a flowchart of an embodiment for explaining a beamforming coefficient determination method in a wireless communication system according to the present invention.
FIG. 3 is a flowchart of an embodiment for explaining a step of determining a beamforming coefficient using the dispersion matrix shown in FIG.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the relevant art and are to be interpreted in an ideal or overly formal sense unless explicitly defined in the present application Do not.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to facilitate the understanding of the present invention, the same reference numerals are used for the same constituent elements in the drawings and redundant explanations for the same constituent elements are omitted.

Throughout the specification, the network can be, for example, a wireless Internet such as WiFi (wireless fidelity), a wireless broadband internet (WiBro) or a portable internet such as world interoperability for microwave access (WiMax) A 3G mobile communication network such as Wideband Code Division Multiple Access (WCDMA) or CDMA2000, a high speed downlink packet access (HSDPA), or a high speed uplink packet access (HSUPA) A 3.5G mobile communication network, a 4G mobile communication network such as an LTE (Long Term Evolution) network or an LTE-Advanced network, and a 5G mobile communication network.

Throughout the specification, a terminal is referred to as a mobile station, a mobile terminal, a subscriber station, a portable subscriber station, a user equipment, an access terminal, And may include all or some of the functions of a terminal, a mobile station, a mobile terminal, a subscriber station, a mobile subscriber station, a user equipment, an access terminal, and the like.

Here, a desktop computer, a laptop computer, a tablet PC, a wireless phone, a mobile phone, a smart phone, a smart watch, smart watch, smart glass, e-book reader, portable multimedia player (PMP), portable game machine, navigation device, digital camera, digital multimedia broadcasting (DMB) A digital audio recorder, a digital audio player, a digital picture recorder, a digital picture player, a digital video recorder, a digital video player ) Can be used.

Throughout the specification, a base station is referred to as an access point, a radio access station, a node B, an evolved node B, a base transceiver station, an MMR mobile multihop relay) -BS, and may include all or some of the functions of a base station, an access point, a radio access station, a Node B, an eNodeB, a base transceiver station, and a MMR-BS.

1 is a block diagram illustrating one embodiment of a station performing methods in accordance with the present invention.

Referring to FIG. 1, a station 100 may include at least one processor 110, a memory 120, and a network interface device 130 for communicating with a network. In addition, the station 100 may further include an input interface device 140, an output interface device 150, a storage device 160, and the like. Each component included in the station 100 may be connected by a bus 170 and communicate with each other.

The processor 110 may execute a program command stored in the memory 120 and / or the storage device 160. The processor 110 may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which the methods of the present invention are performed. The memory 120 and the storage device 160 may be composed of a volatile storage medium and / or a non-volatile storage medium. For example, memory 120 may be comprised of read only memory (ROM) and / or random access memory (RAM).

Generally, the optimal beamforming coefficient in the receiving multiple antennas is calculated from the product of the inverse of the dispersion matrix and the crossover variance vector, which satisfies the minimum mean square error. At this time, the method of directly obtaining the inverse of the dispersion matrix and finding the coefficients is called a direct matrix inverse operation method in a manner different from general adaptive filter methods. When using this method, the process of obtaining the inverse of the dispersion matrix requires a lot of computational complexity. That is, the computational complexity required to obtain the inverse of an n-dimensional square matrix is proportional to the cube of n. In order to obtain the optimal beamforming coefficient satisfying the minimum mean square error, the inverse of the dispersion matrix must be calculated. The size of the dispersion matrix is equal to the number of receive antennas. Therefore, as the number of receiving end antennas increases to 8, 12, 16, etc., the computational complexity for obtaining the inverse of the dispersion matrix increases exponentially.

In the present invention, in order to lower the computational complexity, a circulant approximation is used to determine a beamforming coefficient through adjustment of a time domain factor and an azimuth domain factor. This method uses the property that the dispersion matrix corresponds to the Toeplitz matrix. When the dispersion matrix is approximated by the circulation matrix, the inverse of the dispersion matrix can be simply obtained by a small amount of calculation. However, it can be numerically unstable when the dispersion matrix is approximated by a circulant matrix. Therefore, the error due to the circular approximation can be minimized by conditioning the time domain factor and the azimuth domain factor.

2 is a flowchart of an embodiment for explaining a beamforming coefficient determination method in a wireless communication system according to the present invention.

Referring to FIG. 2, a variation matrix corresponding to a vector component of a received signal is approximated by a circulant matrix (S200).

일때 각 안테나 요소로부터 수신되는 신호를 벡터로 표현하면 다음의 수학식 1과 같다. Assume a receiving antenna array with n number of antennas. The azimuth of the received signal

A signal received from each antenna element is expressed by a vector as follows.

는 i번째 안테나 요소에서 더해지는 가우시안 백색잡음이며, K는 상수로서 마이크로웨이브의 파장 및 안테나 어레이에서 안테나 사이의 간격과 관련된 값이다. 이때, 최소 평균 자승 에러를 만족하는 최적의 빔포밍 계수

는 다음의 수학식 2에 의해 산출된다. In Equation (1), x is a transmission symbol,

Is the Gaussian white noise added at the i < th > antenna element and K is a constant related to the wavelength of the microwave and the spacing between the antennas at the antenna array. At this time, the optimal beamforming coefficient satisfying the minimum mean square error

Is calculated by the following equation (2).

는 에르미트(Hermitian) 연산자이고,

는 i번째 안테나 요소에서의 빔포밍 계수이며,

는

와

사이의 교차분산으로서

로 표현된다. 여기서

은 가우시안 백색잡음

의 분산이며

는 디렉 델타(dirac-delta) 함수이다. 상기 수학식 2에서 좌변이 분산행렬이고 우변이 교차분산 벡터이다. 상기 수학식 2에서 보듯이 최적의 빔포밍 계수를 구하기 위해서는 크기가 n인 분산행렬의 역을 구해야 하므로 수신 안테나의 개수가 증가할수록 연산 복잡도는 지수적으로 증가하게 된다. In Equation (2)

Is a Hermitian operator,

Is the beamforming coefficient at the i < th > antenna element,

The

Wow

As the cross dispersion

Lt; / RTI > here

Silver Gaussian white noise

Dispersion of

Is a dirac-delta function. In Equation (2), the left side is a dispersion matrix and the right side is a crossover dispersion vector. As shown in Equation (2), in order to obtain the optimal beamforming coefficient, the inverse of the dispersion matrix of size n must be obtained. Therefore, the computational complexity increases exponentially as the number of receiving antennas increases.

To solve this problem, a dispersion matrix corresponding to a vector component of a received signal is approximated by a circulant matrix. As an example of approximating a dispersion matrix to a circulation matrix, all the columns constituting the dispersion matrix can be approximated by a circulation matrix. The first column can be approximated as Equation (3) so that the dispersion matrix of Equation (2) becomes a circulation matrix.

First, after approximating the first column of the dispersion matrix as shown in Equation (3), the other columns of the dispersion matrix can be approximated to be a circulating matrix.

At this time, the step of approximating the dispersion matrix to the circulation matrix can be approximated to the circulation matrix after adjusting the time domain factor of the dispersion matrix. Equation (4) below is an expression for explaining the adjustment of the time domain factor.

만큼 증가시킨 후에, 분산 행렬을 순환 행렬로 근사화할 수 있다. The step of adjusting the time domain factors to approximate the scattering matrix to the circulating matrix may be performed by using the first row of at least one column of each of the columns of the scattering matrix

, The variance matrix can be approximated by a recursive matrix.

만큼 증가하도록 수정하는데, 시간 컨디셔닝 값

는 컨디셔닝의 정도를 의미한다. 일반적으로 시간 컨디셔닝 값

은 1보다 작은 양의 실수일 때 컨디셔닝으로 인한 오차를 줄이면서도 수치적으로 안정적이다. 이와 같이, 시간 컨디셔닝 값

를 수정한 후, 분산 행렬의 다른 열들에 대해서도 순환 행렬이 되도록 근사화한다. 시간 도메인 인자를 컨디셔닝한다는 의미는 분산벡터의 첫번째 엔트리 값을 크게 함으로써(방위각 도메인에서 DC값을 더하는 것에 해당함), 방위각 도메인의 모든 각도에 대해 양의 바이어스를 가하는 것이다. 그렇게 함으로써, 후술하는 바와 같이, 분산 행렬을 구성하는 대각행렬의 역을 취할 때 수치적으로 불안정해지는 것을 방지할 수 있다.Referring to equation (4), the first entry of the variance matrix is

, The time conditioning value < RTI ID = 0.0 >

Means the degree of conditioning. Typically, the time conditioning value

Is numerically stable while reducing the error due to conditioning when the real number is less than one. As such, the time conditioning value

And approximates the other columns of the variance matrix to be a circulation matrix. Conditioning the time domain factor means applying a positive bias to all angles of the azimuth domain by increasing the first entry value of the variance vector (equivalent to adding a DC value in the azimuth domain). By doing so, it is possible to prevent numerical instability when taking the inverse of the diagonal matrix constituting the dispersion matrix, as will be described later.

After step S200, a beamforming coefficient is determined using an approximated dispersion matrix (S202). FIG. 3 is a flowchart of an embodiment for explaining a step (S202) of determining a beamforming coefficient using the dispersion matrix shown in FIG.

Referring to FIG. 3, the approximated distributed matrix is decomposed into an inverse discrete Fourier transform matrix, a diagonal matrix, and a discrete Fourier transform matrix (S300). If a dispersion matrix approximated by a circulation matrix is expressed as COV_AP, COV_AP is expressed by Equation (5) from the properties of the circulation matrix.

는 사이즈가 n×n인 이산 푸리에 변환 행렬이며,

는 역 이산 푸리에 변환 행렬이고,

는 n차원 입력벡터를 n×n 대각 행렬로 만드는 함수이다. 순환 행렬로 근사화된 분산 행렬을 이용해서 수학식 2를 표현하면 다음의 수학식 6과 같다.In Equation (5)

Is a discrete Fourier transform matrix having a size of n x n,

Is an inverse discrete Fourier transform matrix,

Is a function that converts an n-dimensional input vector into an n × n diagonal matrix. Equation (2) can be expressed as Equation (6) using a dispersion matrix approximated by a circulation matrix.

, 대각 행렬

및 이산 푸리에 변환 행렬

로 분해된 것을 확인할 수 있다.That is, as described in Equation (6), if the approximated dispersion matrix is an inverse discrete Fourier transform matrix

, Diagonal matrix

And a discrete Fourier transform matrix

. ≪ / RTI >

After step S300, the beamforming coefficient is determined by adjusting the azimuth domain factor of the inverse diagonal matrix with respect to the diagonal matrix (S302).

Equation (6) can be expressed by the following Equations (7) and (8).

As described in Equation (8), the discrete Fourier transform and the inverse discrete Fourier transform can be easily performed even with a simple calculation amount through a fast Fourier transform method. On the other hand, the inverse of the diagonal matrix is easily obtained by reversing the diagonal elements. As a result, as described in Equation (8), it is possible to easily calculate the beamforming coefficient through fast Fourier transform without calculating the inverse of the highly complex n-dimensional dispersion matrix.

However, in order to solve this problem, it is necessary to adjust the azimuth domain factor of the inverse diagonal matrix in order to reduce the accuracy of the beamforming coefficient.

는 다음의 수학식 9와 같이 표현할 수 있다.In Equation (8)

Can be expressed by the following equation (9).

Since the matrix of Equation (9) is a diagonal matrix, values other than diagonal elements are not important. The inverse of the diagonal matrix of Equation (9) is given by Equation (10).

의 대각 성분 엔트리에 대해 각각 역을 취하면 된다. 따라서, 이 값들이 여러 요인들(예를 들어, 양자화 오차, 채널 추정 오차 등)로 인하여 0에 가까운 값이 된다면 수치적으로 매우 불안정하다. 따라서, 역 대각 행렬의 방위각 도메인 인자에 대한 컨디셔닝을 위해 다음의 수학식 11 내지 13이 사용된다.As can be seen in equation (10), the inverse of the diagonal matrix is

The diagonal entry of the diagonal element may be reversed. Therefore, if these values are close to zero due to various factors (for example, quantization error, channel estimation error, etc.), they are numerically very unstable. Therefore, the following equations (11) to (13) are used for conditioning the azimuth domain factor of the inverse diagonal matrix.

은 방위각 도메인 컨디셔닝의 정도를 의미한다. 방위각 컨디셔닝 값

은 1보다 작은 양의 실수로 하여 방위각 도메인 인자 B를 조정할 수 있다. 특히, 방위각 컨디셔닝 값

은 통상적으로 1보다 상당히 작은 값으로 하여 컨디셔닝으로 인한 오차를 줄이면서도 수치적으로 안정적이 되도록 한다. 즉, 방위각 도메인 컨디셔닝의 의미는 수학식 9의 대각 행렬의 엔트리들이 지나치게 작아지는 것을 방지하기 위해, 양의 바이어스를 가함으로써 수치적으로 안정되게 하는 것이다. 이와 같이, 방위각 도메인 인자 B의 조정에 따라

를 수정한 후, 수학식 8에 의한 빔포밍 계수를 결정할 수 있다. In Equation 12, the azimuthal conditioning value of the azimuth domain factor B

Means the degree of azimuth domain conditioning. Azimuth conditioning value

Can adjust the azimuth domain factor B with a positive real number less than one. In particular, the azimuth conditioning value

Will typically be a value significantly less than one so that the error due to conditioning is reduced while still being numerically stable. That is, the meaning of the azimuthal domain conditioning is to make the entries of the diagonal matrix of Equation (9) numerically stable by applying a positive bias to prevent the entries from becoming too small. Thus, according to the adjustment of the azimuth domain factor B

And the beamforming coefficient according to Equation (8) can be determined.

The methods according to the present invention can be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the computer readable medium may be those specially designed and constructed for the present invention or may be available to those skilled in the computer software.

Examples of computer readable media include hardware devices that are specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate with at least one software module to perform the operations of the present invention, and vice versa.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims. It will be possible.

Claims (7)

Approximating a variation matrix corresponding to a vector component of a received signal by a circulant matrix; And
Determining a beamforming coefficient using the approximated scattering matrix,
The step of approximating the dispersion matrix to the circulation matrix
After adjusting the time domain factor of the dispersion matrix, approximating the time domain factor with the circulation matrix, wherein at least one column of each of the columns of the dispersion matrix

, And then approximating the beamforming coefficients by the circulation matrix. The method of claim 1, wherein approximating the scattering matrix to the circulating matrix comprises:
Wherein all the columns constituting the dispersion matrix are approximated by the circulation matrix. delete delete The method according to claim 1,
remind

Among the time conditioning values

Is a positive real number less than one. Approximating a variation matrix corresponding to a vector component of a received signal by a circulant matrix; And
Determining a beamforming coefficient using the approximated scattering matrix,
Wherein the step of determining the beamforming coefficient using the approximated scattering matrix comprises:
Decomposing the approximated distributed matrix into an inverse discrete fourier transform matrix, a diagonal matrix, and a discrete Fourier transform matrix; And
And adjusting the azimuth domain factor of the inverse diagonal matrix for the diagonal matrix to determine the beamforming coefficient. The method of claim 6,
Wherein the inverse diagonal matrix is expressed by Equation

, The equation

Function of

≪ / RTI >

Azimuth conditioning value of

Is adjusted to be a positive real number smaller than 1, and the azimuth domain factor B is adjusted.

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