KR102223168B1 - Detection method for mimo communication - Google Patents

Abstract

The present invention relates to an IF detection method using Alamoti speech in vehicle-to-vehicle MIMO communication, and a detection method for MIMO communication between k transmitting nodes including a plurality of antennas and receiving nodes including N antennas In the received vector filtering step; Decoding the nearest integer; Intermediate variable calculation step; And calculating a decoding vector. It may include.

Description

Detection method for MIMO communication {DETECTION METHOD FOR MIMO COMMUNICATION}

The present invention relates to a detection method for MIMO communication, and more particularly, to an IF detection method using Alamoti phonetic words in vehicle-to-vehicle MIMO communication.

Transportation Network The rapid development of transportation puts great pressure on traffic safety, transportation efficiency and sustainable development of the city. At the same time causing enormous economic losses, it also causes traffic accidents, increased environmental pollution, and other traffic problems.

The relevant traffic management department has taken various measures, such as rules for odd and even number plates, auctioning licenses, and raising parking fees, but it is still costly. The transportation system is a fairly complex system, and it is difficult to solve the problem by separately considering a vehicle or a road.

Intelligent Transportation Systems (ITS) is a comprehensive technology that integrates various computers, sensing technologies, and communication technologies into the management and control of the transportation system. This can effectively utilize existing transportation facilities to make the transportation system safer, more efficient and more reliable.

In essence, vehicle networking is a wired-required sensor network where each vehicle can be considered a super sensor node. Wireless sensor networks consist of a large number of randomly configured sensor nodes, and network range is one of the most basic problems.

ITS wireless communication mainly relies on short-range wireless communication and long-distance mobile communication technology that mainly uses mobile communication technologies such as 3G (3rd generation communication system), 4G (4th generation communication system), and LTE (Long Term Evolution).

With the rapid development of big data, cloud computing and wireless communication technologies, network support is provided for ITS' specific service applications.

Many storage resources in cloud computing are used to protect sensitive data. As one of the important elements of ITS, vehicle-to-vehicle (V2V) communication is being studied.

In general, vehicle-to-vehicle communication in ITS can be divided into V2V and V2I. As shown in FIG. 1, the V2V communication system transmits signals through a high-speed wireless network including vehicle speed, direction, geographic location, route, etc. using a vehicle equipped with a transmission device. Other vehicles receive the transmitted radio signal in real time and feed back similar information that constitutes the information exchange process. By sharing real-time information between vehicles, the system can react more timely to avoid danger and further enhance traffic safety.

V2V systems can pose risks if the driver is not aware of it in various road conditions by communicating with nearby vehicles. Vehicles in emergency braking, for example, transmit the signal broadly to nearby vehicles so that they can take action automatically to avoid an accident, which is more accurate and faster than the driver's judgment. This can theoretically reduce the accident rate by more than 70%.

However, when the vehicle moves quickly, the Doppler phenomenon becomes serious. Especially in real urban traffic environments, tall buildings, bridges, tunnels and green belts attenuate the channels more seriously along with high speed driving. When the two vehicles move rapidly with each other, the polarity of the Doppler frequency shift reverses within a short time, resulting in channel degradation. While one vehicle is traveling with a tall building and other reflective objects around it, different paths of the same transmitted signal are superimposed at the receiver to form multipath fading, which can seriously affect signal strength and communication quality. In most vehicle scenarios, especially urban scenes, the channels of the V2V system have very dynamic characteristics due to multipath fading.

Traditional wireless communication technology cannot easily solve the above problems. In recent years, MIMO (Multi Input Multi Output) technology, which has two or more antennas in the transmitter and receiver, can achieve real-time communication between vehicles by meeting higher speed requirements and reducing the transmission delay of emergency messages.

In addition, MIMO communication uses space time block code (STBC) and other signal processing techniques such as V-BLAST (Vertical-Bell Labs Layered Space-Time) coding and space time coding to provide more reliable communication for ITS applications. Diversity benefits can be used to do so. Beamforming technology can spatially converge the transmitted signal and greatly expand the communication range, which is more useful in low vehicle density scenarios. Therefore, the MIMO technology is well applied to V2V communication and improves anti-padding and anti-locking functions to improve channel capacity and transmission reliability.

Assume that the transmitter and receiver are each equipped with two or more antennas, as shown in Figure 2. The input signal is modulated and transmitted through a transmit antenna, and the receiver receives the multi-channel signal superposition and resolves the original signal through effective detection and demodulation. Although the MIMO technology has many advantages, the detection method used in the related art is related to the system performance and computational complexity.

However, the conventional MIMO detection method has a problem in that the tradeoff between system performance and computational complexity is not good. That is, the detection method in which the performance of the system is emphasized has a problem in that the computational complexity is excessively high, and the detection method with low computational complexity has a problem in that the performance is degraded.

In order to solve the above problems, the MIMO detection method according to an embodiment of the present invention has an object to ensure that both computational complexity and performance are at a certain level or higher in IF detection using Alamouti phonetic words.

In addition, the MIMO detection method according to an embodiment of the present invention has a lower computational complexity than the conventional ML detection and has another object to provide a detection method capable of outputting better performance than a linear test.

In addition, the MIMO detection method according to an embodiment of the present invention has another object to provide maximum diversity gain in a given MIMO communication environment.

를 계산하는 수신 벡터 필터링 단계; 상기 필터링된 수신 벡터

를 최근접 정수인

로 디코딩하여 전송된 코드워드의 정수 선형 조합을 구하는 최근접 정수 디코딩 단계; 상기 최근접 정수

에 대하여

모듈로 연산을 수행함으로써 중간 변수 r을 구하는 중간 변수 계산 단계; 및 상기 중간 변수 r에 상기 정수 행렬 A의 역행렬을 통해 디코딩된 벡터

를 구하는 디코딩 벡터 계산 단계; 를 포함하고, 상기 중간 변수 계산 단계에서 M은 수신 노드의 변조 차수이고, 상기 디코딩 벡터 계산 단계에서 상기 정수 매트릭스 A 가역 행렬일 수 있다.In the MIMO detection method according to an embodiment of the present invention for solving the above problem, in the detection method for MIMO communication between k transmission nodes including a plurality of antennas and a reception node including N antennas, an effective filter Received vector filtered through filter matrix B for matrix B and integer matrix A and received vector y

A receiving vector filtering step of calculating a; The filtered received vector

Is the nearest integer

A nearest integer decoding step of obtaining an integer linear combination of the transmitted codewords by decoding with; The nearest integer

about

An intermediate variable calculation step of obtaining an intermediate variable r by performing a modulo operation; And a vector decoded through the inverse matrix of the integer matrix A in the intermediate variable r.

A decoding vector calculation step to obtain a; In the step of calculating the intermediate variable, M may be a modulation order of the receiving node, and in the step of calculating the decoding vector, the integer matrix A may be a reversible matrix.

The integer matrix A may be calculated using a CLLL (Complex Lenstra Lenstra Lovasz) lattice reduction algorithm.

을 식

으로 변경하는 모델 변경 단계; 를 더 포함하고, 상기 모델 변경 단계에서, 상기

는 수신 노드의 평균 신호대 잡음 비인 P를 상기 k 개의 전송 노드 전체의 안테나 수인 M_S로 나눈 값이고, 상기 X는 완전 전송 행렬이고, 상기 H는 채널 행렬을 의미하며, 상기 W는 잡음 매트릭스를 의미하고, 상기 G는 상기 채널 행렬 H의 원소를 이용하여 계산된 행렬일 수 있다.Prior to the vector filtering step, the equation for the system model

Expression

Model change step to change to; Further comprising, in the model changing step, the

Is a value obtained by dividing P, which is the average signal-to-noise ratio of the receiving node, by M _S , the number of antennas of all the k transmitting nodes, where X is a complete transmission matrix, H is a channel matrix, and W is a noise matrix. And, G may be a matrix calculated using the elements of the channel matrix H.

The MIMO detection method according to an embodiment of the present invention has an effect of ensuring both computational complexity and performance at a certain level or higher in IF detection using Alamoti phonetic words.

In addition, the MIMO detection method according to an embodiment of the present invention has a lower computational complexity than conventional ML detection and can output better performance than a linear test.

In addition, the MIMO detection method according to an embodiment of the present invention has an effect of providing maximum diversity gain in a given MIMO communication environment.

However, the effects that can be achieved by an embodiment of the present invention are not limited to those mentioned above, and other effects not mentioned are obvious to those of ordinary skill in the art from the following description. It will be understandable.

1 is a diagram for briefly explaining vehicle-to-vehicle communication.
2 is a diagram for explaining a MIMO system model.
3 is a diagram illustrating a multiple transmission node system.
4 is a block diagram illustrating a MIMO detection method according to an embodiment of the present invention.
5 is a flowchart illustrating a MIMO detection method according to an embodiment of the present invention.
6 to 8 are graphs for comparing a detection method according to an embodiment of the present invention with a conventional detection method under different conditions.

In the present invention, various modifications may be made and various embodiments may be provided, and specific embodiments are illustrated in the drawings, and these will be described in detail through detailed description. However, this is not intended to limit the present invention to specific embodiments, and the present invention should be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the present invention.

In describing the present invention, when it is determined that a detailed description of a related known technology may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted. In addition, numbers (eg, first, second, etc.) used in the description of the present specification are merely identification symbols for distinguishing one component from other components.

In addition, in the present specification, when one component is referred to as "connected" or "connected" to another component, the one component may be directly connected or directly connected to the other component, but specially It should be understood that as long as there is no opposite substrate, it may be connected or may be connected via another component in the middle.

Hereinafter, exemplary embodiments according to the inventive concept will be described with reference to the drawings.

3 is a diagram illustrating a multiple transmission node system. Prior to describing an embodiment of the present invention, a multiple transmission node system according to MIMO communication will be described.

를 만족한다. 그리고, 수신 노드는 N 개의 안테나를 구비할 수 있다. 채널은 레일리 페이딩(Rayleigh fading)에 따라 분산되어 있는 것으로 가정한다. 상기 페이딩은 독립적이고, 채널 상태 정보(Channel State Information: CSI)는 리시버에 의해 알려진 상태이다. H_k는 전송 노드 k와 수신 노드 간의 채널 매트릭스를 의미할 수 있다. H_k는

를 만족하며, H_k의 원소들은 IID(Independent Identically Distributed) 콤플렉스 표준 가우시안 분포를 따른다. T-시간 슬롯 블록에 있어서, 채널이 일정하다고 가정하여 다음 블록에서 독립적으로 실현된다.It is assumed that one receiving node exists at the same time from K transmitting nodes transmitting data as shown in FIG. 3. Transmitting nodes k (k is a natural number of 1 to K) each have M _k antennas (where k may mean a node index). That is, the total number of antennas M _S of the transmitting node is

Is satisfied. In addition, the receiving node may have N antennas. It is assumed that the channels are distributed according to Rayleigh fading. The fading is independent, and channel state information (CSI) is a state known by the receiver. H _k may mean a channel matrix between a transmitting node k and a receiving node. H _k is

Is satisfied, and _{the elements of H k} follow the standard Gaussian distribution of the IID (Independent Identically Distributed) complex. In the T-time slot block, it is independently realized in the next block assuming that the channel is constant.

가 T_MK로 정규화한 에너지인 전송 STBC 행렬을 나타내는 경우 수신된 신호 T 시간 슬롯 내의

는 다음 [수식 1]과 같이 표현될 수 있다.In an embodiment of the present invention, Alamouti Space-Time Block Code (STBC) is used in all transmission nodes. Here, _{the value of M k} may be 2 and the value of T may be 2. The transmission vector S _k is [s ₁ , s ₂ ..., s _2mk ] ^T is the actual vector of _{2M k} dimension corresponding to the M _k -dimensional complex transmission of the symbol. The actual symbol vector s _k is constrained to obtain a value from an integer constellation with a modulation order M. if,

Is the transmitted STBC matrix, which is the energy normalized by _{T MK, the received signal T within the time slot}

Can be expressed as the following [Equation 1].

[Equation 1]

는 리시버에서의 평균 신호대 잡음 비율을 의미하는 P를 M_S로 나눈 값일 수 있다. X(S_k) = X_k 는 전송 노드 k 의 전송 행렬일 수 있다. 완전 전송 행렬 X 는

를 만족하고, 채널 행렬 H는

를 만족할 수 있다. 그리고, 잡음 행렬

을 만족하며, 모두 IID 콤플렉스 표준 가우시안 분포를 따를 수 있다.here

May be a value obtained by dividing P, which means the average signal-to-noise ratio in the receiver, by M _S. X(S _k ) = X _k may be the transmission matrix of the transmission node k. The complete transmission matrix X is

And the channel matrix H is

Can be satisfied. And, the noise matrix

And all can follow the standard Gaussian distribution of the IID complex.

For the convenience of measuring the error rate, the channel model can be changed to the actual form of [Equation 2] as follows.

[Equation 2]

를 만족하며, 2개의 전송 노드를 예로들면, G 는 다음과 [수식 3]같이 설명될 수 있다.here

Satisfying, and taking two transmission nodes as an example, G can be described as follows [Equation 3].

[Equation 3]

Here, H _k(mn) may mean an element in the m-th row and n-th column of the channel matrix Hk. And this can be rewritten as shown in [Equation 4] below.

[Equation 4]

이다. here

to be.

4 is a block diagram illustrating a MIMO detection method according to an embodiment of the present invention. Instead of directly detecting the transmission symbol, the IF receiver initially detects an integer combination of the corresponding symbol, thereby reducing noise amplification to some extent as shown in FIG. 4.

In the MIMO detection method according to an embodiment of the present invention, one of the key steps is to find a preprocessing matrix B that makes the channel matrix H as approximate as possible, BH ≒ A. To recover Alamoti phonetic words using the IF algorithm, 2 NT = 2M _S must be satisfied, and the integer matrix A must be a reversible matrix.

의 서브셋 값을 취하여 격자로부터 만든 격자 코드다.Transmission vector s is

It is a grid code created from the grid by taking a subset of values.

When the received vector y in FIG. 4 is multiplied by the filter matrix B, G can be projected onto a nonsingular integer matrix A.

The projected signal can be expressed as [Equation 5] as follows.

[Equation 5]

는 유효 잡음을 의미한다. [수식 5]의 시스템 모델은 종래의 선형 검파 방법이나 ZF 검파 방법에 사용될 수 있다.Where As is the linear combination of transmitted codes,

Means effective noise. The system model of [Equation 5] can be used in a conventional linear detection method or ZF detection method.

For convenience, the effect noise intensity of the i-th layer may be defined as follows [Equation 6].

[Equation 6]

는 컨스틸레이션(constellation)의 평균 에너지다. 층당 유효 잡음 편차를 최소화하려면 g(a_i,b_i)를 최소로 만들기 위해 적절한 행렬 A와 B를 생성해야 한다. 여기서 행렬 A는 CLLL(Complex Lenstra Lenstra Lovsz) 알고리즘을 이용하여 생성될 수 있다. CLLL은 해당 기준으로 최단 벡터를 얻는 것을 목표로 한다. Where a _i and b _i are row vectors representing the i-th row of matrices A and B, respectively,

Is the average energy of the constellation. To minimize the effective noise variance per floor, we need to create appropriate matrices A and B to _{minimize g(a i} ,b _{i ).} Here, the matrix A may be generated using a CLLL (Complex Lenstra Lenstra Lovsz) algorithm. CLLL aims to get the shortest vector by that criterion.

Also, as another method, matrices A and B can be calculated as follows.

[Equation 7]

By using the matrix L, the matrices A and B can be calculated as follows.

5 is a flowchart illustrating a MIMO detection method according to an embodiment of the present invention. 5, the MIMO detection method according to an embodiment of the present invention includes a reception vector filtering step (S100), a nearest integer decoding step (S200), an intermediate variable calculation step (S300), and a decoding vector calculation step (S400). Can include. In addition, a model change step may be performed prior to the vector filtering step.

를 계산하는 단계일 수 있다.In the vector filtering step (S100), the effective filter matrix B and the integer matrix A are obtained, and the received vector filtered through the filter matrix B for the received vector y

It may be a step of calculating.

를 최근접 정수인

로 디코딩하여 전송된 음어의 정수 선형 조합을 구하는 단계일 수 있다.The nearest integer decoding step (S200) is the filtered received vector

Is the nearest integer

It may be a step of obtaining a linear combination of integers of phonetic words transmitted by decoding with.

에 대하여

모듈로 연산을 수행함으로써 중간 변수 r을 구하는 단계일 수 있다.The intermediate variable calculation step (S300) is the nearest integer

about

It may be a step of obtaining an intermediate variable r by performing a modulo operation.

를 구하는 단계일 수 있다. The decoding vector calculation step (S400) is a vector decoded through the inverse matrix of the integer matrix A in the intermediate variable r.

It may be a step to obtain.

Here, in the step of calculating the intermediate variable, M is a modulation order of the receiving node, and in the step of calculating the decoding vector, the integer matrix A may be a reversible matrix.

Diversity gain for the detection method can be calculated as follows.

는 다음과 [수식 8]과 같이 계산될 수 있다.Other steps are determined in the above decoding step except for the nearest integer decoding step S200, which is related to recovering a linear combination of code values from noise. As a result, the received vector of the i-th layer in the nearest integer decoding step (S200)

Can be calculated as follows [Equation 8].

[Equation 8]

는

의 i 번째 행을 나타낸다. 그리고,

는 양자화 잡음에 대한 용어로서, 완성된 유효 잡음은 양자화 잡음으로 인해 가우스 분포를 따르지 않게 된다.here

Is

Represents the i-th row of. And,

Is a term for quantization noise, and the completed effective noise does not follow a Gaussian distribution due to quantization noise.

For a fixed a _i , the optimal b _i can be used to minimize the effective noise intensity g(a _i , b _{i ), which can be expressed as Equation 9 below.}

[Equation 9]

이고, b_i는

와 같이 단순하게 표현될 수 있다. 여기서 P는 큰 값을 가질 수 있으며,

는 무시할 수 있을 만큼의 값을 가진다. 이러한 방식으로 큰 값인 P의 케이스에서 유효 잡음 세기는 다음 [수식 10]과 같이 표현될 수 있다.here,

And b _i is

It can be expressed simply as Where P can have a large value,

Has a negligible value. In this way, in the case of P, which is a large value, the effective noise intensity can be expressed as the following [Equation 10].

[Equation 10]

More generally, the diversity gain by STBC measurement focuses primarily on cases that are large numbers.

에 있어서, i 번째 계층의 에러율은

로 정의될 수 있다. 그리고 IF 검파 방법의 최대 에러율은 다음 [수식 11]과 같이 정해질 수 있다.Unlimited grid

In, the error rate of the i-th layer is

Can be defined as And the maximum error rate of the IF detection method can be determined as follows [Equation 11].

[Equation 11]

는 격자

의 스퀘어 유클리드 거리를 의미할 수 있다.Where c is the constant independence of P,

The lattice

Can mean square Euclidean distance.

는 선형 디자인 X_LD에 의해 생성될 수 있고,

로 정의될 수 있다.

의 특이한 케이스로서, 유한

도 선형 디자인 X_LD에 의해 생성될 수 있다.infinite

Can be generated by a linear design X _LD,

Can be defined as

As an unusual case of, finite

Also can be produced by a linear design X _LD.

으로부터 생성될 수 있다. 즉,

를 의미한다.However, the transmitted code is a fixed constellation

Can be generated from In other words,

Means.

과 같이 정의하면, 유한 STBC의 최소 특이 값은

과 같이 표현될 수 있다.The least singular value of X

If defined as, the least singular value of finite STBC is

It can be expressed as

Below, the diversity gain assumes that all transmitting nodes have two antennas. The information can first be encrypted by Alamoti STBC as follows.

In addition, in a receiver having one antenna and using the IF detection method, the transmission matrix of node j can be expressed as follows.

이고, 여기서

는 다음 [수식 12]와 같이 표현될 수 있다.Under the condition where P is a large number, the maximum value of the error rate that can be obtained using the IF detection method in STBC is

Is, where

Can be expressed as the following [Equation 12].

[Equation 12]

를 만족한다. d의 원소들은 반드시 S에 포함될 필요는 없다. G가 H의 동등한 식인 것에 대응하여,

는 다음과 같이 더 표현될 수 있다. here

Is satisfied. The elements of d need not necessarily be included in S. Corresponding to G being the equivalent expression of H,

Can be further expressed as follows.

[Equation 13]

를 만족하며, XX^H는 특별히 다음과 같이 표현될 수 있다.here

Is satisfied, and XX ^H can be specifically expressed as follows.

으로 표현될 수 있다. 여기서

는

의 스퀘어 특이 값을 포함하는 사선 행렬을 의미한다. X가 알라모티 블록으로 구성되므로,

는 다음과 같이 표현될 수 있다.XX ^H is in particular through Singular Value Decomposition.

It can be expressed as here

Is

It means a diagonal matrix containing the square singular values of. Since X is composed of Alamoti blocks,

Can be expressed as

는 다음과 같이 표현될 수 있다.here

Can be expressed as

의 특이 값의 스퀘어는 X_-k가 알라모티 블록인 이상,

을 만족할 수 있다.

The square of singular values of X _-k is Alamoty Block Inn,

Can be satisfied.

에 따라,

과

는 선형적으로

과 같이 표현될 수 있다.

Depending on the,

and

Is linearly

It can be expressed as

는 다음과 같이 표현될 수 있다.Especially

Can be expressed as

는 다음과 같이 변환될 수 있다.therefore,

Can be converted to

는 다음 [수식 14]과 같이 표현될 수 있다.Here, since U is a unitary matrix, H and R can have the same distribution. Since R = HU,

Can be expressed as the following [Equation 14].

[Equation 14]

는 X₁의 특이 값의 스퀘어를 의미한다.

을 만족하는 이상,

는 [수식 15]와 같이 표현될 수 있다.Here, the value of M is 2, and the ith column of R and

Denotes the square of the singular value of X _1.

Ideal for satisfying,

Can be expressed as [Equation 15].

[Equation 15]

의 최소 값을 에러 율의 최대 값에 대입하면, 다음과 같은 [수식 16]을 얻을 수 있다.

Substituting the minimum value of in to the maximum value of the error rate, the following [Equation 16] can be obtained.

[Equation 16]

는 모두 P와 관련 없는 상수이다. 행렬 H와 R이 동일한 분포를 가지는 이상 표준 일반 분포

는 chi-스퀘어 분포를 따른다.CP and

Are all constants not related to P. Ideal standard normal distribution where matrices H and R have the same distribution

Follows chi-square distribution.

[Equation 17]

를 만족하기 때문에, P가 큰 값을 가지는 이상 1은 무시될 수 있다.

Since P is satisfied, 1 can be neglected as long as P has a large value.

[Equation 18]

[Equation 18] represents the error rate of one layer. The sum of the error rates can be expressed as follows.

[Equation 19]

After the prediction as described above is made, the average error rate of the nearest integer decoding step (S200) in the MIMO detection method according to an embodiment of the present invention may be expressed as the following [Equation 20].

[Equation 20]

를 만족한다. here

Is satisfied.

의 다양성 이득을 달성할 수 있고 이는 최대 값을 의미한다. 따라서, M개의 전송 안테나를 포함하는 K 개의 노드들과 N 개의 수신 안테나를 구비한 알라모티 음어를 이용하는 MIMO 통신에서, 최대 다양성 이득

이 본 발명의 실시예에 따른 검파 방법에 의해 구해질 수 있음을 알 수 있다.Through the measurement, the detection method according to the embodiment of the present invention

The diversity gain of can be achieved, which means the maximum value. Therefore, in MIMO communication using K nodes including M transmit antennas and Alamoti phonetic words including N receive antennas, the maximum diversity gain

It can be seen that this can be obtained by a detection method according to an embodiment of the present invention.

6 to 8 are graphs for comparing a detection method according to an embodiment of the present invention with a conventional detection method under different conditions.

6 is a comparison between a detection method (IL) according to an embodiment of the present invention and a bit error rate (BER) according to a conventional ML detection method in V-BLAST. In FIG. 6, it is assumed that each transmitting node has two antennas, and that the receiving node also has two antennas.

7 is a graph comparing an IF detection method according to an Alamoti phonetic word and BER according to an ML detection method in a situation where the value of K of a transmission node is 2.

8 is a graph comparing an IF detection method according to an Alamoti phonetic word and a BER according to an ML detection method in a situation where the value of K of a transmission node is 3;

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features. You can understand that there is. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects.

Claims (4)

In the detection method for MIMO communication at a reception node including N antennas communicating with k transmission nodes including a plurality of antennas,
The received vector filtered by multiplying the received vector (y ) by a predetermined filter matrix ( B) (

Including the step of calculating ),
(BG) according to the equation below to calculate the constant matrix (A) and the constant matrix (A) and the filter matrix (B) which approximates, as to estimate the transmission matrix (s),

Here, G is the channel matrix between the transmitting node and the receiving node, s is the transmission matrix, w is the noise matrix, M _S is the total number of antennas of k transmitting nodes, and P is the average signal-to-noise ratio at the receiving node. For detection method. delete The method of claim 1,
The integer matrix (A) is a reversible matrix, a detection method for MIMO communication. The method of claim 1,
The detection method is a detection method for MIMO communication for applying when decoding an Alamoti phonetic word using an Integer-Force (IF).

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