KR101556482B1 - Method and Apparatus for INTERFERENCE ALIGNMENT METHOD WITH SLNR MAXIMIZATION IN MIMO INTERFERENCE CHANNEL - Google Patents

Abstract

Disclosed are a method and an apparatus for interference alignment by maximizing SLNR in a multi-antenna interference channel. The apparatus for interference alignment by maximizing SLNR comprises a pair of a transmitter and a receiver which comprise a multi-antenna participating in interference alignment. The pair of the transmitter and the receiver may include: a free-coding matrix generation unit which generates an eigen-vector with respect to a signal space, required by the transmitter, and an inverse of signal leakage power if the pair of the transmitter and the receiver operate as a receiver; and a decoding matrix generation unit which generates a unique vector with respect to a signal space, required by the receiver, and an inverse of a signal leakage power.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an interference alignment method and apparatus for maximizing SLNR in a multi-antenna interference channel,

The present invention relates to a method and apparatus for an interference alignment technique in a network composed of multiple input multiple output (MIMO) systems.

의 최대 DoF를 가질 수 있다. 따라서 두 개의 사용자가 존재하는 간섭채널에서 개별 노드가 갖게 되는 이론적인 용량합(Sum Capacity)는

이 된다. 여기서 SNR은 완벽한 채널정보(CSI : Channel State Information)를 제공할 수 있는 값이다.The performance of a wireless system is limited by interference between multiple links on the frequency spectrum. Therefore, the problem of how to control interference becomes an important issue to deal with in wireless networks. Interference Alignment (IA) is one of the most promising research areas in recent years as a way to maximize the overlap of all interferences received at each receiver. The purpose of the interference alignment is to place the signal to be received in the signal space of each receiver at half of the signal space and the signals of the remaining transmitter to be placed at half of the signal space by interference. That is, put all the interferences into a common subspace of the received signal space and make the interference space and the desired signal space linearly independent. Two users with multiple antennas in a multi-antenna (MxN) interference channel (MIMO-IC) can use zero-forcing techniques for each user

Quot; DoF " of < / RTI > Therefore, the theoretical capacity of the individual nodes in the interference channel in which two users exist is

. Here, the SNR is a value that can provide complete channel information (CSI: Channel State Information).

The interference alignment technique is a closed-form method and an iterative method. The non-repetitive technique has a DoF of M / 2 limited to three users having even (M) transmitting and receiving antennas. An iterative technique is to optimize the precoding and decoding matrices alternately at the transmitter and the receiver. An iterative technique, which may be referred to as a distributed technique, requires only partial channel information on the assumption that the transmission and reception channels of the wireless network are the same. In this paper, we propose an interference alignment technique that minimizes the interference alignment in the signal space and maximizes the SINR by orthogonalizing the signal subspace and the interference signal subspace in each receiver. Also, an algorithm that minimizes the interference alternately according to the gradient direction of the sum-rate, and a method using the MMSE have been studied. However, since most iterative algorithms assume a high SNR, it is necessary to study the interference sorting algorithm at low SNR.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a method and apparatus for continuously reducing signal leakage power at each transmitter, And to provide a method and apparatus for maximizing power. The precoding and decoding matrices are designed to maximize the SLNR and SINR, respectively, and reduce the power leakage of the received signal and the power leakage of the received interfering signal. In each iteration, the power leakage is minimized and the algorithm converges. However, it may take some delay to maximize and converge the signal power within the desired signal subspace.

The proposed SLNR maximization algorithm converges into a precoding and decoding matrix that maximizes the SLNR in each transmitter, which means that the power of the desired signal is maximized and the power leakage in each transmitter is reduced. By calculating the optimal precoding matrix and decoding matrix in the manner suggested by the present patent in each iteration, the power of the signal can be maximized in the subspace of the desired signal and the power leakage can be minimized.

In one aspect, the interference alignment apparatus for maximizing SLNR in a multi-antenna interference channel proposed in the present invention is composed of a pair of a transmitter and a receiver composed of multiple antennas participating in indirect alignment, A precoding matrix generator for generating an eigenvector for a desired signal space and an inverse of a signal leakage power in the transmitter when the pair of receivers and the receiver operate as a transmitter; And a decoding matrix generator for generating an eigenvector for the desired signal space and the inverse of the signal leakage power.

The desired signal subspace and the undesired signal subspace may be separated by maximizing the SLNR used in the precoding matrix generator and the decoding matrix generator.

When the precoding and decoding are generated by repeatedly maximizing the SLNR used in the precoding matrix generator and the decoding matrix generator, the capacity sum performance can be maximized beyond a specific number of iterations.

According to another aspect of the present invention, there is provided a method of maximizing SLNR interference in a multi-antenna interference channel, comprising: receiving a decoding matrix through a receiver; estimating a subspace of a desired signal and a signal power leakage subspace Generating a precoding matrix using an eigenvector from a maximum value for the signal, transmitting the generated precoding via the transmitter, using an eigenvector for the desired signal space and the inverse of the signal leakage power at the receiver Generating a decoding matrix, receiving the decoding matrix, and repeating the decoding until the decoding matrix converges.

The receiver may receive a signal using the decoding matrix generated by the decoding matrix generator.

The transmitter may transmit a signal using the precoding matrix generated using the eigenvectors through SLNR maximization in the precoding generator.

The receiver may receive the signal using a decoding matrix generated using an eigenvector through SLNR maximization of the signal received by the decoding generator.

According to embodiments of the present invention, in generating the precoding matrix and the decoding matrix using the eigenvectors through maximizing the SLNR, it is necessary to repeatedly perform the series of processes until convergence, By separating the undesired signal subspace and the subspaces of the desired signal, transmitting / receiving devices participating in the interference alignment can simultaneously perform communication.

1 is a diagram illustrating a network comprised of multiple antenna systems participating in interference alignment in accordance with an embodiment of the present invention.
FIG. 2 is a flowchart of an interference alignment method by maximizing SLNR in a multi-antenna interference channel according to an embodiment of the present invention.
3 is a diagram showing a capacity sum according to SNR at (2X2, 1) ³ according to an embodiment of the present invention.
4 is a diagram showing a capacity sum according to SNR in (4X4, 1) ³ according to an embodiment of the present invention.
5 is a diagram showing a capacity sum according to an increase in the number of repetitions in (2X2, 1) ³ according to an embodiment of the present invention.
6 is a diagram showing a capacity sum according to an increase in the number of repetitions in (4X4, 1) ³ according to an embodiment of the present invention.

SUMMARY OF THE INVENTION The present invention is an interference alignment technique in a network composed of multiple input multiple output (MIMO) systems. More specifically, when a signal is transmitted simultaneously with other users by using SLNR (Signal to Leakage and Noise Ratio) in a multi-antenna system network, mutual interference is aligned so that signals of other users in the signal space can be put into the interference space, And finally achieving a degree of freedom (DoF) of 1/2 for each individual node pursued in the interference alignment.

The present invention consists of a transmitter-receiver pair consisting of multiple antennas participating in an interference alignment. Each of these transceivers comprises a precoding generator for calculating a precoding matrix and a decoding generator for calculating a decoding matrix. The precoding generator generates an eigenvector for the desired signal space and the inverse of the signal leakage power at the transmitter when the transceiver operates as a transmitter. The decoding generator generates an eigenvector for the desired signal space and the inverse of the signal leakage power at the receiver when the transceiver operates as a receiver.

First, the receiver receives a signal using an arbitrary decoding matrix, and generates and transmits a precoding matrix using an eigenvector through SLNR maximization in a precoding generator. Then, the receiver generates a decoding matrix using the eigenvectors by maximizing the SLNR for the signal received by the decoding generator, and receives the decoding matrix. This series of processes proceeds until the precoding and decoding matrices converge. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a network comprised of multiple antenna systems 100 participating in an interference alignment in accordance with an embodiment of the present invention.

Wherein the pair of transmitter 102 and receiver 103 comprises a pair of transmitter 102 and receiver 103 configured with multiple antennas participating in indirect alignment and wherein the pair of transmitter 102 and receiver 103 is a pair of transmitter 102 and receiver 103, And a precoding matrix generator for generating an eigenvector for a desired signal space and an inverse of a signal leakage power in the transmitter when the transmitter operates. In addition, when the pair of the transmitter 102 and the receiver 103 operates as a receiver, the receiver may include a decoding matrix generator for generating an eigenvector for a desired signal space and an inverse of signal leakage power.

The desired signal subspace and the undesired signal subspace may be separated by maximizing the SLNR used in the precoding matrix generator and the decoding matrix generator. When the precoding and decoding are repeatedly performed by repeatedly maximizing the SLNR used in the precoding matrix generator and the decoding matrix generator, the capacity sum performance can be maximized beyond a specific number of iterations.

Referring again to FIG. 1, a schematic diagram 100 illustrating a network comprised of multiple antenna systems participating in an interference alignment comprises a transmitter 102 and a receiver 103 with respective antennas 101.

) 송신기-수신기 쌍은

개의 송신 안테나와

개의 수신 안테나를 갖고

개의 공간 스트림을 전송한다. 보다 구체적으로는 k번째 송신기기는 k번째 수신 기기와 1대1로 짝 지워져 동시에 K개의 링크가 발생하는 것이다. 각각의

개의 안테나를 갖는 송신기는 그에 해당하는

개의 수신 안테나에 신호를 송신하고 그에 따라 그림 1에서 보여지듯이 의도되지 않은 수신기들에 대해 동일채널 간섭(co-channel interference)을 발생시킨다. 모든 기기들이 동일한 안테나 구성을 갖고 있다고 할 때, 즉,

일 때 대칭적인 MIMO-IC 네트워크를

로 정의 한다. 본 논문에서 채널은 협대역 블록 페이딩 MIMO 간섭채널모델로 간주하였고, 여기서 네트워크 상의 모든 채널 링크들은 한 심볼의 전송구간 동안 불변이고 그 다음 심볼 전송구간에서는 바뀌는 것으로 가정하였다. 수신기기 k의 채널출력은 수학식 1과 같다.
Considering K symmetric MIMO-IC networks, the kth (

) The transmitter-receiver pair

≪ / RTI >

Have a receiving antenna

Lt; / RTI > More specifically, the k-th transmitter is paired with the k-th receiving device on a one-to-one basis, and K links are generated simultaneously. Each

Transmitters with two antennas are

Signal to the receive antennas, thereby causing co-channel interference to unintended receivers as shown in FIG. When all devices have the same antenna configuration, that is,

A symmetric MIMO-IC network

. In this paper, we assume that the channel is a narrow-band block fading MIMO interfering channel model, where all channel links on the network are assumed to be invariant during the transmission interval of one symbol and then changed in the next symbol transmission interval. The channel output of the receiving device k is expressed by Equation (1).

수학식 1

Equation 1

는 송신기 l과 수신기 k간에 독립적이고 동일한 분포(IID : Independently and identically distributed)를 갖는 MIMO 복소수 채널 페이딩 계수 행렬이고,

은

의 전송심볼

을 송신하는데 사용되는 l번째 송신기의 프리코딩 행렬이다. 여기서

는 k번째 링크의 DoF이다. 그리고,

는 0의 평균값과

의 잡음분산 값을 가지며

,

가 되는, 수신기 k에서의 IID 복소 가우시안 잡음 벡터를 나타낸다. 추가적으로 채널

들은 각각 full rank를 가지며 상호간 독립적이고, 전송심볼은 IID를 가정하였다. 또한, 이러한 신호 모델에서 심볼타이밍의 동기화를 오차 없이 수행하는 캐리어 복구를 가정하였다. 그리고2 전개의 편의를 위해 각 링크의 전송 전력은 1로 정규화시켰다.here

Is a MIMO complex channel fading coefficient matrix that is independent and identically distributed (IID) between transmitter l and receiver k ,

silver

The transmission symbol

Lt; RTI ID = 0.0 > l < / RTI > here

Is the DoF of the kth link. And,

Lt; RTI ID = 0.0 > 0 &

≪ / RTI >

,

Is the IID complex Gaussian noise vector at receiver k . Additionally,

Have full rank and are mutually independent, and the transmission symbol is assumed to be IID. Also, we assume carrier recovery to perform synchronization of symbol timing in this signal model without error. For the convenience of two deployments, the transmission power of each link is normalized to one.

라고 정의하여, k번째 수신기의 수신기 필터 출력을 수학식 2와 같이 정의할 수 있다.
The decoding matrix of the kth receiver is

The receiver filter output of the kth receiver can be defined as Equation (2).

수학식 2

Equation 2

And the Sum-Rate performance of the kth transceiver pair using interference alignment in the MIMO-IC network is defined as: < EMI ID = 3.0 >

수학식 3

Equation 3

는 수신신호의 공분산 행렬을 나타내고

는 k번째 수신기의 간섭 및 잡음 공분산 행렬을 나타낸다. 그리고, 대부분의 간섭정렬 알고리즘들은 프리코딩과 디코딩 행렬이 직교하도록 설계되고, 각각의

는

로 정의 된다.here

Represents a covariance matrix of the received signal

Represents the interference and noise covariance matrix of the kth receiver. And most of the interference-ordering algorithms are designed such that the precoding and decoding matrices are orthogonal,

The

.

를 갖는 프리코딩 행렬

와

를 갖는 디코딩 행렬

이 다음 수학식 4와 수학식 5를 만족할 때 가능하다.
Implementation in linear interference alignment

Lt; RTI ID = 0.0 >

Wow

/ RTI >

Is satisfied when the following equations (4) and (5) are satisfied.

수학식 4

Equation 4

수학식 5

Equation 5

Equation (4) is a condition in which a signal space in which interference does not exist exists, and Equation (5) indicates a condition in which a desired signal can be found.

In this patent, when each transmitter and receiver has multiple antennas and there are interfering channels originating from a pair of K transceivers, maximize the signal power within the subspace of the signal to be transmitted at each transmitter, We propose a method to minimize power leakage. The SINR at the receiver is defined as Equation (6).

수학식 6

Equation 6

The SINR for k = 1, ..., K in the above equation can be used as an optimization criterion for defining a precoding and decoding matrix so that the SINR is maximized at each receiver.

로 계산할 수 있고, 수신기 l에서 송신기 k의 신호를 수신 받아 간섭으로 작용하는 신호전력은

로 계산된다. 따라서 송신기 k를 제외한 송신기들에서 수신기 k로 누설되는 총 간섭전력은

로 계산할 수 있다. 제안하는 방법의 목적은 송신하고자 하는 신호전력

가 다른 사용자들로의 누설전력

보다 크게 만드는 것이다. 이러한 방법은 SLNR이라 불리는 평가수치로써 수학식 7과 같이 표현될 수 있다.
Another approach of the IA for MIMO-IC is to minimize the leakage power from each transmitter to the unwanted receiver. From Equation (2), the signal power to be transmitted from transmitter k to receiver k is

, And the signal power received by the receiver l from the transmitter k and acting as interference is

. Therefore, the total interference power leaked from the transmitters except for transmitter k to receiver k is

. The purpose of the proposed method is to estimate the signal power

Leakage power to other users

It will make it bigger. This method can be expressed as Equation (7) with an evaluation value called SLNR.

수학식 7

Equation 7

와 달리, 위 식의

은 수신기가 아닌 송신기에서 계산되는 값이다. 수학식 7은 수학식 8과 같이 정리되어 쓰여질 수 있다.

Unlike the above,

Is the value calculated at the transmitter rather than at the receiver. Equation (7) can be summarized as Equation (8).

수학식 8

Equation 8

는 l번째 수신기기에서의 잡음분산 값을 나타낸다.here

Represents the noise variance value at the 1 < th >

Maximization of the SLNR for all K transmissions included in the interference alignment in the MIMO-IC system minimizes the leakage power received at each receiver by minimizing the leakage power to the unwanted receiving devices at each transmitter, The SINR for the device is also continuously improved. Using this leakage power concept, the present invention solves the interference alignment problem in the MIMO-IC using the optimization problem defined by Equation (9).

수학식 9

Equation 9

이고,

는 k번째 송신에 할당된 총 전력을 나타낸다.here

ego,

Represents the total power allocated to the kth transmission.

로부터 시작한다. 그리고 레일레이-리츠 나머지 정리(Rayleigh-Ritz quotient result)로부터 수학식 9의 문제는 수학식 10과 같이 해결할 수 있고,
Designing a closed form solution in the optimization problem of Equation (9) is a difficult problem because of the interdependency of the received signal subspace of the precoder, decoder, and receiver. The problem of the above equation can be easily accessed by an iterative technique that uses a method of minimizing the crossing of the precoding and decoding matrices. First,

Lt; / RTI > From the Rayleigh-Ritz quotient result, the problem of Equation (9) can be solved as Equation (10)

Equation 10

(11).

수학식 11

Equation 11

는 행렬 A의 가장 큰 고유값(eigen value)에서

개의 고유값(

)에 해당하는 고유 벡터를 나타낸다. 즉, 최적 프리코딩 행렬은

와

에서

개의 큰 고유값에 해당하는

개의 고유벡터로 얻을 수 있다. 여기서 SLNR은

개의 최대 고유값들(

)에 의해 최대화된다.here

Is the largest eigenvalue of matrix A

Unique values (

). ≪ / RTI > That is, the optimal precoding matrix is

Wow

in

Corresponding to the largest eigenvalues of

Eigenvectors. Where SLNR is

The maximum eigenvalues (

).

를 구하거나, 최적 디코딩 행렬을 얻는데 있어서 채널 호혜성을 요구하지 않는 SINR 최대화를 통해 간섭을 정렬할 수 있다. 본 특허에서는 채널호혜성에 대한 요구를 없애는 것에 관심이 있기 때문에 각 수신기에서 디코딩 행렬

을 구하기 위해 SINR의 최대화를 수행하고 이는 수학식 12와 같이 정의된다.
The purpose of interference alignment is to allow each receiver to limit the interference of all other transmitters to a certain portion of the subspace in the receive space of the receiver to decode the signal it wishes to receive. The maximization of the SLNR at each transmitting device is to minimize the signal leakage power to other unwanted receiving devices participating in the interference alignment. However, the receiving apparatus can not arrange the transmission signals from the remaining K-1 transmitters by interference. To this end, a decoding matrix for a reciprocal network assuming channel reciprocity

Or to maximize the SINR that does not require channel reciprocity in order to obtain an optimal decoding matrix. In this patent, since it is interested in eliminating the demand for channel reciprocity, the decoding matrix

The SINR maximization is performed, which is defined as Equation (12).

수학식 12

Equation 12

에 대한 최적의 디코딩행렬

을 구하는 것은 SINR 최대화와 비슷한 방식이다. 따라서 디코딩 행렬들

은 프리코딩 행렬을 갱신하면서 얻게 된다. 이러한 반복은 알고리즘이 수렴할 때까지 계속된다. 프리코딩 행렬들을 설계하기 위한 SLNR 최대화와 디코딩 행렬들을 설계하기 위한 SINR 최대화는 채널호혜성에 대한 요구를 제거할 수 있다.
The given precoding matrices

The optimal decoding matrix for

Is similar to SINR maximization. Therefore,

Is obtained by updating the precoding matrix. This iteration continues until the algorithm converges. Maximizing SLNR for designing precoding matrices and maximizing SINR for designing decoding matrices can eliminate the need for channel reciprocity.

FIG. 2 is a flowchart of an interference alignment method by maximizing SLNR in a multi-antenna interference channel according to an embodiment of the present invention.

An interference alignment method via SLNR maximization in a multi-antenna interference channel includes receiving (201) a decoding matrix through a receiver, using the eigenvector from the maximum value for the partial space of the desired signal and the signal power leakage partial space in the transmitter (202) generating a precoding matrix by the transmitter, transmitting (203) the generated precoding through the transmitter, and using the eigenvector of the desired signal space and the inverse of the signal leakage power in the receiver (204), receiving the decoding matrix (205), and repeating (206) until the decoding matrix converges.

로 수신(201)하는 것으로부터 시작된다. 그리고, 수학식 11과 같이 송신기기 k의 원하는 신호의 부분공간과 신호전력누수부분공간의 역(다시 말해, SLNR)에 대한 최대값으로부터

개의 고유벡터를 사용하여 프리코딩 행렬

를 생성(202)하여 전송(203)한다. 그리고 이를 수신한 수신기는, 수학식 12와 같이 수신기기 l의 원하는 신호의 부분공간과 신호전력누수부분공간의 역(즉, SLNR)에 대한 최대값으로부터

개의 고유벡터를 사용하여 디코딩 행렬

를 생성(204)하여 수신(205)한다. 그리고 이를 수렴할 때까지 반복한다.For the first time an arbitrary decoding matrix

(201). ≪ / RTI > Then, as shown in Equation (11), from the maximum value of the partial space of the desired signal of the transmitter k and the inverse of the signal power leakage partial space (i.e., SLNR)

Lt; RTI ID = 0.0 > eigenvectors < / RTI >

(202) and transmits (203). Then, the receiver receives the signal s from the maximum value of the partial space of the desired signal of the receiving device 1 and the inverse of the signal power leakage partial space (i.e., SLNR)

Lt; RTI ID = 0.0 > eigenvectors < / RTI &

(204) and receives (205). Repeat until convergence.

Table 1 is simulation parameters for demonstrating the performance of the present invention.

<Table 1>

로 동일한 것으로 가정하였다. K개의 수신하고자 하는 채널링크와 K(K-1)개의 간선채널링크에 대한 채널계수들은 0의 평균값과 1의 복소가우시안 랜덤변수 분산을 갖는 IID로 가정하였다. 또한 각 기기의 전력 제한은

로 동일하게 설정하였다.
In a network of multi-antenna systems, it is assumed that three transceiver pairs participate in the interference alignment, and the transceiver antenna

. The channel coefficients for K receive channel links and K (K-1) adjacent channel link are assumed to be IIDs with an average value of 0 and a complex Gaussian random variable variance of 1. Also, the power limit of each device is

.

3 is a diagram showing a capacity sum according to SNR at (2X2, 1) ³ according to an embodiment of the present invention.

FIG. 3 is a graph illustrating performance of a (2 x ² , 1) ³ MIMO-IC network. The SLNR maximization technique is optimal and the max sum-rate technique degrades at a low SNR. Can be confirmed. In addition, the SINR maximization technique (max SINR) and the SINR sum maximization technique (max sum SINR) show that the performance is worse than the proposed method at SNR of 0 ~ 20dB. In the distributed algorithm, the performance improves at the low SNR, the SNR increases and the performance increases. The MMSE algorithm has the optimal performance at the low SNR, and the alternating minimization is the worst Performance.

4 is a diagram showing a capacity sum according to SNR in (4X4, 1) ³ according to an embodiment of the present invention.

FIG. 4 shows performance of (4 × 4, 1) ³ MIMO-IC networks, and the proposed algorithm shows optimal performance over the entire SNR region. Up to 10dB, the performance is the same as that of the SINR maximization technique, and the SINR sum maximization technique has lower performance than the proposed algorithm. The distributed algorithm, the SINR sum maximization method, and the cross minimization method show the best performance only over 25dB, and the MMSE algorithm has the same performance as (2ⅹ2, 1) ³ .

5 is a diagram showing a capacity sum according to an increase in the number of repetitions in (2X2, 1) ³ according to an embodiment of the present invention.

In the (2 x 2, 1) ³ MIMO-IC network of Fig. 5, all algorithms converge similarly, except that the capacity sum values are different.

6 is a diagram showing a capacity sum according to an increase in the number of repetitions in (4X4, 1) ³ according to an embodiment of the present invention.

In the case of the (4 x 4, 1) ³ MIMO-IC network of FIG. 6, it can be seen that the SLNR maximization technique has a larger capacity sum as the number of iterations increases, resulting in a constant performance improvement in the entire SNR region. The proposed algorithm does not show the difference between SINR maximization and performance in (2 ⅹ 2, 1) ³ MIMO-IC, but it can improve the performance when the number of antennas is larger.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in 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 to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically 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 produced 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 as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI &gt; or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (7)

A pair of transmitters and receivers composed of multiple antennas participating in indirect alignment,
The pair of transmitter and receiver may comprise:
When a pair of the transmitter and the receiver operates as a transmitter, a precoding matrix for maximizing the SLNR through an eigenvector for a desired signal space and a signal leakage power in the transmitter is generated using Equation (1) A matrix generator; And
Generating a decoding matrix for maximizing an SINR using an eigenvector for a desired signal space and an inverse of a signal leakage power at a receiver using Equation (2), when the transmitter and the receiver are operating as a receiver, part
Lt; / RTI &gt;
The precoding matrix and the decoding matrix are repeatedly generated for the transmitter and receiver pairs, the desired signal subspace and the undesired signal subspace are separated through the SLNR maximization, and the SNR maximization and the SINR maximization are performed, Wherein the channel reciprocity requirement for the interference cancellation channel is eliminated.
[Equation 1]

&Quot; (2) &quot;

Where H _lk denotes a MIMO complex channel fading coefficient matrix having independent and identically distributed (IID) independent between transmitter k and receiver l , V _k denotes a precoding matrix generated in transmitter k , U _l denotes a decoding matrix generated in the receiver l ,

Denotes the noise variance value of the IID complex Gaussian noise vector, and _dk denotes the DoF (Degree of Freedom) of the kth link. delete The method according to claim 1,
Wherein when the precoding and decoding are generated by repeatedly performing the SLNR maximization used in the precoding matrix generator and the SINR maximization used in the decoding matrix generator to maximize the capacity sum performance over a specific number of iterations, An interference alignment device through SLNR maximization in an antenna interference channel. Receiving a decoding matrix through a receiver;
Generating a precoding matrix using Equation (3) to maximize the SLNR through the eigenvectors from the maximum value for the inverse of the desired signal's subspace and the signal power leakage subspace at the transmitter;
Transmitting the generated precoding via the transmitter;
Generating a decoding matrix for maximizing the SINR using an eigenvector for a desired signal space and an inverse of signal leakage power in the receiver using Equation (4);
Receiving the decoding matrix; And
Repeating the decoding until the decoding matrix converges
Lt; / RTI &gt;
The precoding matrix and the decoding matrix are repeatedly generated for the transmitter and receiver pairs, the desired signal subspace and the undesired signal subspace are separated through the SLNR maximization, and the SNR maximization and the SINR maximization are performed, Wherein the channel reciprocity requirement for the multi-antenna interference channel is eliminated.
&Quot; (3) &quot;

&Quot; (4) &quot;

Where H _lk denotes a MIMO complex channel fading coefficient matrix having independent and identically distributed (IID) independent between transmitter k and receiver l , V _k denotes a precoding matrix generated in transmitter k , U _l denotes a decoding matrix generated in the receiver l ,

Denotes the noise variance value of the IID complex Gaussian noise vector, and _dk denotes the DoF (Degree of Freedom) of the kth link. 5. The method of claim 4,
Wherein the receiver receives a signal using the decoding matrix generated in the step of generating the decoding matrix. 5. The method of claim 4,
Wherein the transmitter transmits a signal using a precoding matrix generated using an eigenvector through SLNR maximization in the step of generating the precoding matrix, wherein the transmitter is configured to maximize SLNR in a multiple antenna interference channel. 5. The method of claim 4,
Wherein the receiver receives a signal using a decoding matrix generated using an eigenvector through SINR maximization in the step of generating the decoding matrix.

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