KR20100057177A - Apparatus and method for zero-forcing beamforming in distributed mimo system - Google Patents

Abstract

PURPOSE: A device and a method for zero-forcing beam forming in a distributed MIMO system are provided to maximize transmission capacity in consideration of the maximum power limit condition per a base station during downlink transmission of a ZF beam forming mode. CONSTITUTION: A power allocation unit(306) allocates power to transmission data in consideration of a maximum power condition per a base station. A precoder(308) performs zero-forcing beam forming to transmit the transmission data to which the power is allocated. A channel information matrix feedback receiver(300) receives channel information matrices from terminals. A transmission mode determiner(302) calculates a power allocation coefficient vector.

Description

Apparatus and Method for Optimal Zero-Forced Beamforming in Distributed Multiple Input / Output Systems {APPARATUS AND METHOD FOR ZERO-FORCING BEAMFORMING IN DISTRIBUTED MIMO SYSTEM}

The present invention relates to an apparatus and method for optimal ZF beamforming in a distributed MIMO system, and more particularly to a ZF beamforming for multiple users in a distributed MIMO system in which geographically dispersed base stations are connected by wire or dedicated line. The present invention relates to an apparatus and method for maximizing a transmission capacity in consideration of a maximum power constraint that can be transmitted per base station in the downlink transmission of the scheme.

Recently, as the demand for high-speed and high-quality data transmission has increased, a multiple input multiple output (MIMO) technique using a plurality of transmit / receive antennas is one of the techniques for satisfying this. . The MIMO technology is a technology that can significantly improve the channel capacity than when using a single antenna by performing communication using a plurality of channels due to a plurality of antennas. For example, if both the transmitting and receiving ends use M transmit antennas and receive antennas, the channels between each antenna are independent, and the bandwidth and the total transmit power are fixed, the average channel capacity is increased by M times compared to a single antenna. .

The MIMO technology may be divided into a single user MIMO (hereinafter referred to as "SU-MIMO") technology and a multiple user MIMO (hereinafter referred to as "MU-MIMO") technology. The SU-MIMO technology is for a pair of transceivers to perform one-to-one communication by occupying a plurality of channels by a plurality of antennas, and the MU-MIMO technology uses a plurality of channels by a plurality of antennas. By dividing and using, it is for performing communication between transmitting and receiving ends having a one-to-many relationship.

In the MU-MIMO system, a zero-forcing beamforming method, in which a transmitting end multiplies a reciprocal of a channel and transmits a transmission signal, is mainly used to reduce interference of another user or another antenna. When the ZF beamforming method is used in the MU-MIMO system, the transmitting end may set power differently for each antenna under a total power constraint condition. For example, if a transmitter uses four antennas and can use a total of 20 W of power, each antenna may be allocated 6 W, 4 W, 8 W, and 2 W of power.

Meanwhile, the distributed MIMO system refers to a system in which a plurality of geographically dispersed units are connected by wire or dedicated line to share information of a terminal and transmit data using the same frequency band. Here, the unit may be one of a base station, a relay station, and a separate amplifier. Hereinafter, the base station will be described as an example.

As a transmission method capable of downlink transmission of the distributed MIMO system, a signal to interference plus noise ratio is achieved through a single antenna transmission (SAT) for a single user and cooperative transmission between transmitting antennas. Ratio (hereinafter referred to as 'SINR') has been widely studied. In particular, the cooperative transmission scheme includes maximum ratio transmission (MRT) that maximizes received SINR, equal transmission of transmission power between transmitting antennas equally (Equal Gain Transmission: hereinafter referred to as 'EGT'), and space-time block coding. (Space-Time Block Coding: hereinafter referred to as 'STBC'), etc., and when the transmitting antennas are geographically distributed as in the distributed MIMO system, there is a power limitation that can be independently transmitted per antenna. And equal power transmission scheme between antennas such as STBC is suitable. When applying a variety of transmission schemes for such a single user, by selectively using the transmission antenna according to the instantaneous channel situation, it is possible to increase the SINR and the transmission capacity accordingly, especially for users in the boundary area between base stations. In the distributed MIMO system, such a single user transmission scheme may be appropriately used to support services for geographically unevenly arranged users through scheduling and antenna selection techniques.

In addition, when the instantaneous channel for a plurality of users is represented as a matrix, when the channel is well-conditioned, the distributed MIMO system uses a ZF beamforming scheme for a plurality of users instead of the single user transmission scheme. By applying it, more transmission capacity can be obtained. In the case of the ZF beamforming method, since the interference between the target users is all removed, the transmission capacity depends on the amount of external interference. Especially, when the ZF beamforming between all antennas in the network is applied, the interference between the antennas is canceled out so that the transmission is very large. Capacity can be obtained.

However, unlike the MU-MIMO system, the distributed MIMO system has a fixed power that can be used per base station. Therefore, if the ZF beamforming method of the MU-MIMO system is applied to the distributed MIMO system, the following problems may occur. Can be. For example, assuming that only a maximum of 5W per base station can be used, in the example of the MU-MIMO system, 6W, 4W, 8W, and 2W power should be allocated to each antenna. However, in the distributed MIMO system, 6W, You will not be able to support 8W.

Therefore, there is a need for proposal of an optimal ZF beamforming method suitable for the distributed MIMO system.

Accordingly, an object of the present invention is to provide an apparatus and method for optimal ZF beamforming in a distributed MIMO system.

Another object of the present invention is to provide a maximum power constraint that can be transmitted per base station when transmitting ZF beamforming downlink signals for multiple users in a distributed MIMO system in which geographically dispersed base stations are connected by wire or dedicated lines. It is an object of the present invention to provide an apparatus and method for maximizing a transmission capacity.

Another object of the present invention is to selectively use a ZF beamforming method showing superior performance in a high transmission capacity region and an LQ decomposition scheme showing excellent performance in a low transmission capacity region according to an instantaneous channel environment in a distributed MIMO system. An object of the present invention is to provide an apparatus and method for improving performance compared to a general ZF beamforming method.

According to an exemplary embodiment of the present invention, a method for transmitting data in a multiple input multiple output (MIMO) system in which geographically dispersed base stations are connected by wire or dedicated line is provided. And allocating power to transmission data in consideration of the maximum power condition, and performing zero-forcing (ZF) beamforming on the transmission data to which the power is allocated. .

According to an embodiment of the present invention to achieve the above object, an apparatus for transmitting data in a multiple input multiple output (MIMO) system in which geographically dispersed base stations are connected by wire or dedicated line, A power allocator for allocating power to transmission data in consideration of a maximum power condition per unit, and a precoder for performing transmission by performing zero-forcing (ZF) beamforming on the transmission data to which power is allocated. It features.

The present invention provides an optimal ZF beamforming method for maximizing transmission capacity in consideration of the maximum power constraint that can be transmitted per base station in a distributed MIMO system in which geographically distributed base stations are connected by wire or dedicated line. The ZF beamforming method in the MIMO system has an advantage of solving problems that may occur when it is applied to the distributed MIMO system as it is. In addition, ZF beamforming method which shows superior performance in the high transmission capacity region and LQ decomposition method which shows superior performance in the low transmission capacity region can be selectively used according to the instantaneous channel environment. There is an advantage that can be improved.

Hereinafter, the operation principle of the preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. Terms to be described later are terms defined in consideration of functions in the present invention, and may be changed according to intentions or customs of users or operators. Therefore, the definition should be made based on the contents throughout the specification.

Hereinafter, a method for maximizing transmission capacity in consideration of the maximum power constraint that can be transmitted per base station when transmitting a ZF beamforming downlink signal for multiple users in a distributed MIMO system will be described.

On the other hand, in the distributed MIMO system, a plurality of base stations are uniformly distributed geographically, and when selecting a transmission base station group for ZF beamforming, another base station is continuously disposed outside thereof, thereby completely eliminating external interference. Transmission is virtually impossible. In addition, since external base stations also transmit the same level of power for services to other users, it can be said to be an environment with a large amount of external interference.

값을 필요로 하지 않는다는 장점이 있다. 또한 알고리듬 상의 반복 과정이 없이 계산되므로 구현 복잡도 측면에서 큰 장점이 있는 방식이다.The ZF beamforming scheme is a transmission scheme with a large performance change in transmission capacity according to orthogonality of a channel matrix. The ZF beamforming scheme has superior performance in a high transmission capacity region but degrades in a low transmission capacity region. Therefore, in an ill-conditioned channel environment that lacks orthogonality in the instantaneous channel matrix, the LQ decomposition that adjusts the amount of interference according to the priority between users while giving up the single user transmission or the orthogonality between users instead of the ZF beamforming method Or QR decomposition). The LQ decomposition scheme has higher performance than the ZF beamforming scheme in a low transmission capacity region, and the amount of external interference during the LQ decomposition process.

The advantage is that no value is required. In addition, this algorithm has a great advantage in terms of implementation complexity because it is calculated without an iterative process.

Accordingly, the present invention, according to the instantaneous channel environment in a distributed MIMO system, selectively selects the ZF beamforming scheme showing superior performance in the high transmission capacity region and the LQ decomposition scheme showing superior performance in the low transmission capacity region. Describe how to use it.

In the following description, distributed MIMO is a method of transmitting data using integrated antennas of a plurality of units, which includes both network MIMO and multi-BS MIMO. to be. Here, the unit may be a base station, a relay station, a separate amplifier, and the like, which will be described below using the base station as an example.

1 is a diagram showing the structure of a distributed MIMO system according to the present invention.

Referring to FIG. 1, the distributed MIMO system includes at least one distributed antenna 101, 102, geographically distributed and capable of communicating with at least one terminal 110, 120, 130, 140, 150. 103 and 104, the distributed antennas (101, 102, 103, 104) and the base station 100 is connected to a wired or dedicated line to communicate with the terminal (110, 120, 130, 140, 150), and the distributed It is configured to include one or more terminals (110, 120, 130, 140, 150) to communicate with the base station 100 through the antenna (101, 102, 103, 104). According to the present invention, the base station 100 allocates an optimal power per antenna to maximize transmission capacity in consideration of the maximum power condition per distributed antenna 101, 102, 103, 104.

2 is a diagram showing another structure of a distributed MIMO system according to the present invention.

Referring to FIG. 2, the distributed MIMO system includes at least one base station 201, 202, 203, and 204 that are geographically distributed and capable of communicating with at least one or more terminals 210, 220, 230, 240, and 250. And a central control unit 200 connected to the base stations 201, 202, 203, and 204 by wire or dedicated line to communicate with the terminals 210, 220, 230, 240, and 250, and the base stations 201, 202. And one or more terminals 210, 220, 230, 240, 250 in communication with the central control unit 200 via 203, 204. According to the present invention, the central control unit 200 allocates an optimal power per base station to maximize transmission capacity in consideration of the maximum power condition per base station 201, 202, 203, 204. The base stations 201, 202, 203, and 204 are connected to each other by wire or dedicated line to share information of the terminals 210, 220, 230, 240, and 250, and all have the same frequency band (i.e., frequency reuse coefficients). Use 1). The base station 201, 202, 203, 204 and the terminal 210, 220, 230, 240, 250 may all have a plurality of antennas, and the terminals 210, 220, 230, 240, 250 in the network may It is possible to receive a single transmission, cooperative transmission, or multi-user simultaneous transmission by ZF beamforming from several base stations 201, 202, 203, and 204 of the.

Hereinafter, an embodiment according to the present invention will be described using the distributed MIMO system of FIG. 2 as an example.

Here, a received signal model for performing ZF beamforming from M base stations having a single transmit antenna to K terminals having a single receive antenna may be expressed by Equation 1 below.

는 수신 신호 벡터이고, 상기

는 송수신 안 테나 사이에 형성되는 네트워크 채널 행렬로서, 원소

는 k번째 단말과 m번째 기지국 사이의 채널 이득을 의미한다. 상기

는 송신 선처리 행렬로서, 상기

의 의사역행렬로 구한다. 상기

는 전력 정규화 계수로서, 전력 할당을 통한 기지국 당 최대 전력 제약 조건을 만족시키고자 하는 전력 할당 최적화 변수이고, 상기

는 k번째 단말로 전송하고자 하는 복소 심볼이다. 상기

는 수신기에서의 열잡음을 포함하는 외부의 복소 간섭 신호이다. Where

Is a received signal vector, and

Is the network channel matrix formed between the transmit and receive antennas,

Denotes a channel gain between the k-th terminal and the m-th base station. remind

Is the transmit preprocessing matrix,

Obtain the pseudo inverse of. remind

Is a power normalization coefficient, which is a power allocation optimization variable for satisfying a maximum power constraint per base station through power allocation.

Is a complex symbol to be transmitted to the k-th terminal. remind

Is an external complex interference signal that includes thermal noise at the receiver.

라 하고, M개의 기지국에서 송신하는 송신 신호 벡터를

라고 하면,

로부터 하기 <수학식 2>와 같은 기지국 당 최대 전송 가능한 전력 제약(per-BS power constraint) 조건 식이 성립하여야 한다. Here, the maximum power that can be transmitted per base station is

The transmission signal vector transmitted from the M base stations

Speaking of

From Equation 2, the maximum per-BS power constraint condition equation per base station must be established.

를 조절하는 것이 총 M개의 수식에 영향을 주므로, 하기 <수학식 3>과 같이 전송 용량 최대화를 목적으로 하는 최적화 문제가 형성된다.As can be seen from Equation 2, the power allocation optimization variable is

Since the control affects the total M equations, an optimization problem for the purpose of maximizing the transmission capacity is formed as shown in Equation 3 below.

은

의 분산으로서 단말 k에서의 간섭 신호 전력값이다. Where

silver

Is the interference signal power at terminal k as the variance of.

의 선형 수식에 대한 로그함수간의 합산 형태이므로 컨벡스(convex) 식이고, 상기 <수학식 2>의 제약 조건식 역시

에 대한 선형 수식이므로 컨벡스 식이다. 따라서 상기 최적화 문제는 컨벡스 문제로서 최적의 해가 항상 존재한다. 라그랑지안 완화(Lagrangian relaxation) 기법을 통해 상기 제약 조건식을 목적함수에 포함시키면서 상기 제약 조건식에 대한 라그랑지(Lagrange) 승수 벡터

를 적용한 라그랑지안 함수는 하기 <수학식 4>와 같이 나타낼 수 있다. Here, the objective function of Equation 3 is

Since it is a summation form between logarithmic functions of the linear equation of, it is a convex expression, and the constraint expression of Equation 2 is also

Since it is a linear equation for, it is a convex expression. Therefore, the optimization problem is a convex problem and an optimal solution always exists. Lagrange multiplier vector for the constraints, including the constraints in the objective function through a Lagrangian relaxation technique

The Lagrangian function to which can be applied can be expressed as Equation 4 below.

에 대해 미분하면 하기 <수학식 5>를 얻을 수 있다. Where Equation 4 is

Differentiating with respect to Equation 5 can be obtained.

는 라그랑지 승수

를 이용하여 계산한다. In other words,

The Lagrange Multiplier

Calculate using

를 계산한다. Here, the Lagrange multiplier is repeatedly updated through a sub-gradient method as shown in Equation 6 below, and the Lagrange multiplier is updated with the updated Lagrange multiplier.

Calculate

는 임의의 양의 상수이며,

을 의미한다. Where

Is any positive constant,

Means.

3 is a block diagram illustrating an apparatus for optimizing a transmission power of a central control unit in a distributed MIMO system according to an exemplary embodiment of the present invention.

As shown, the central control unit includes a channel information matrix feedback receiver 300, a transmission method determination unit 302, an optimal allocation optimization variable calculation unit 304, a power allocation unit 306, a precoder 308, LQ decomposition unit 310 is configured to include.

Referring to FIG. 3, the channel information matrix feedback receiver 300 periodically receives feedback of a channel information matrix from terminals and outputs the channel information matrix to the transmission method determination unit 302.

The transmission method determination unit 302 calculates a pseudo inverse of the channel information matrix from the channel information matrix feedback receiver 300, and calculates a power allocation coefficient vector using the calculated pseudo inverse matrix. That is, a matrix having a power value of each element as an element is calculated from the pseudo inverse matrix, and the power allocation coefficient vector is calculated by multiplying the calculated inverse matrix by the maximum power vector. Thereafter, the transmission method determination unit 302 checks whether all elements of the calculated power allocation coefficient vector are positive, and whether to apply the ZF beamforming method or the LQ decomposition method as the data transmission method. Next, the optimum allocation optimization variable calculation unit 304, the power allocation unit 306, the precoder 308, and the LQ decomposition unit 310 are operated according to the determination result. That is, when all the elements of the calculated power allocation coefficient vector are positive, the application of the ZF beamforming method is determined, and the optimal allocation optimization variable calculation unit 304, the power allocation unit 306, and the precoder 308 are determined. In operation, the optimum allocation optimization variable calculator 304 and the precoder 308 output the calculated pseudo inverse matrix. On the other hand, when all or some elements of the calculated power allocation coefficient vector are not positive, it is determined to apply the LQ decomposition scheme, and the power allocation unit 306 and the precoder 308 and the LQ decomposition unit 310 are determined. In operation, the channel information matrix is output to the LQ decomposition unit 310.

The optimal allocation optimization parameter calculation unit 304 transmits the maximum power constraint equation and the base station using the pseudo inverse matrix when the ZF beamforming scheme is determined as the data transmission scheme by the transmission scheme determination unit 302. An optimal allocation optimization variable that satisfies the objective function for the purpose of maximizing capacity is calculated, and the calculated optimal allocation optimization variable is output to the power allocation unit 306.

When the optimal allocation optimization variable is input from the optimal allocation optimization variable calculator 304, the power allocation unit 306 allocates power to the transmission data using the optimal allocation optimization variable, and then the precoder 308. ) In addition, the power allocator 306 allocates power to transmission data using a general power allocation algorithm when it is determined by the transmission method determination unit 302 that the LQ decomposition method is applied to the data transmission method. The precoder 308 outputs the same.

The precoder 308 uses the pseudo inverse matrix from the transmission method determination unit 302 when the transmission method determination unit 302 determines that the ZF beamforming method is determined as the data transmission method. ZF beamforming is performed. In addition, the precoder 308 is input by using the precoding matrix from the LQ decomposition unit 310 when the transmission method determination unit 302 determines that the LQ decomposition method is applied as the data transmission method. Perform precoding on the data.

When the LQ decomposition scheme is determined by the transmission scheme determination unit 302 as the data transmission scheme, the LQ decomposition unit 310 performs LQ decomposition on the channel information matrix from the transmission scheme determination unit 302. Apply to calculate a precoding matrix, and output the calculated precoding matrix to the precoder 308.

4 is a flowchart illustrating a transmission power optimization method of a central control unit in a distributed MIMO system according to an embodiment of the present invention.

채널 정보 행렬

를 피드백 수신한다. 4, the central control unit periodically from the terminals in step 401

Channel information matrix

Receive feedback.

에 대한 의사역행렬

를 계산한다.Thereafter, the central control unit checks whether there is data to transmit in step 403, and if there is data to transmit, in step 405 the received channel information matrix.

Pseudomatrix for

Calculate

를 이용하여 전력 할당 계수 벡터를 계산한다. 즉, 상기 의사역행렬

로부터 각 원소들의 전력값을 원소로 갖는 행렬

를 계산하고, 상기 계산된

의 역행렬을 최대 전력 벡터

에 곱하여, 전력 할당 계수 벡터

를 계산한다.In step 407, the central control unit calculates the calculated pseudo inverse matrix.

Calculate the power allocation coefficient vector using That is, the pseudo inverse matrix

Matrix of elements with power values from

And calculate the

Inverse of the maximum power vector

Multiply by, power allocation factor vector

Calculate

의 모든 원소가 양수인지 여부를 검사한다. 여기서, 상기 계산된 전력 할당 계수 벡터의 모든 원소가 양수일 시, 상기 중앙 제어 유닛은 이하 411단계 내지 423단계를 통해 최적의 전력 할당 및 ZF 빔포밍(ZF with power boost) 방식을 적용하여 상기 송신 데이터를 전송한다. 즉, 상기 의사역행렬을 이용하여 기지국 당 최대 전력 제약 조건식과 전송 용량 최대화를 목적으로 하는 목적함수를 만족하는 최적 할당 최적화 변수를 계산하고, 상기 최적 할당 최적화 변수를 이용하여 데이터에 전력을 할당한 후, 상기 의사역행렬을 이용하여 프리코딩을 수행한다. 혹은 다른 방법으로, 상기 계산된 전력 할당 계수 벡터

를 그대로 전력 할당 최적화 변수로 사용할 수도 있다. 반면, 상기 계산된 전력 할당 계수 벡터의 모든 혹은 일부 원소가 양수가 아닐 시, 상기 중앙 제어 유닛은 이하 427단계 내지 431단계를 통해 LQ 분해 방식을 적용하여 상기 송신 데이터를 전송한다. 혹은 다른 방법으로, QR 분해 방식을 적용할 수도 있다. The central control unit then calculates the calculated power allocation coefficient vector in step 409.

Tests whether all elements in are positive. In this case, when all the elements of the calculated power allocation coefficient vector are positive, the central control unit applies the optimal power allocation and ZF beamforming (ZF with power boost) method in steps 411 to 423 below. Send it. That is, an optimal allocation optimization variable that satisfies the maximum power constraint expression per base station and an objective function for maximizing transmission capacity is calculated using the pseudo inverse matrix, and power is allocated to data using the optimum allocation optimization variable. Precoding is performed using the pseudo inverse matrix. Or in another way, the calculated power allocation coefficient vector

Can also be used as a power allocation optimization variable. On the other hand, when all or some elements of the calculated power allocation coefficient vector are not positive, the central control unit transmits the transmission data by applying LQ decomposition in steps 427 to 431. Alternatively, QR decomposition can be applied.

의 모든 원소가 양수일 시, 상기 중앙 제어 유닛은 411단계에서 모든 기지국 인덱스에 대한 라그랑지 승수

을 임의의 양수 값으로 초기화한다. 예를 들면

로 초기화한다. In step 409, the calculated power allocation coefficient vector

If all elements of P are positive, the central control unit may use a Lagrange multiplier for all base station indices in step 411.

Initializes to any positive value. For example

Initialize to

을 이용하여 전력 할당 최적화 변수

를 계산한다. 여기서, 상기 n은 라그랑지 승수의 갱신 반복 인덱스로서, 상기 라그랑지 승수 초기화 과정에서 상기 n은 1로 초기화된다. The central control unit then Lagrange multipliers for all the base station index in step 413

Allocation parameters using

Calculate Here, n is an update repetition index of a Lagrange multiplier, and n is initialized to 1 in the Lagrange multiplier initialization process.

는 하기 <수학식 7>과 같이 계산한다. Where the power allocation optimization parameter

Is calculated as in Equation 7 below.

In step 415, the central control unit updates the Lagrange multipliers for all base station indexes.

Here, the Lagrange multiplier is updated by using Equation 8 below.

In step 417, the central control unit checks whether all M Lagrangian multiplier values converge, and when some or all Lagrange multiplier values do not converge, returns to step 415 and repeats the following steps. . On the other hand, when all Lagrangian multipliers converge, the central control unit calculates an optimal allocation optimization variable using Equation 7 in step 419 and uses the calculated optimal allocation optimization variable in step 421. Allocate power to the transmission data.

값들이 0으로 되는 경우를 포함하여 제약 조건 하에서 항상 목적함수를 최대화하는 해를 제시한다. 또한, 상기 <수학식 8>에서의 반복 인덱스 n의 증가에 따라 라그랑지 승수의 변화 폭을 감소시키는 n에 대한 감소함수를 변경하여 알고리듬의 수렴 속도 및 특성을 변화시킬 수 있다.This power allocation algorithm is derived from the convex problem and always converges to an optimal value, which can be demonstrated by the fact that the Karush-Kuhn-Tucker (KKT) condition is always met. That is, under a given instant channel

We always provide solutions to maximize the objective function under constraints, including when the values are zero. In addition, as the repetition index n in Equation 8 increases, the convergence speed and characteristics of the algorithm may be changed by changing a reduction function for n which decreases the variation of the Lagrange multiplier.

In step 423, the central control unit performs precoding on the transmitted data to which power is allocated, using the calculated pseudo inverse matrix, and transmits the precoded data in step 425.

의 모든 원소 혹은 일부 원소가 양수가 아닐 시, 상기 중앙 제어 유닛은 427단계에서 일반적인 전력 할당 알고리듬에 따라 상기 송신 데이터에 전력을 할당하고, 429단계에서 상기

채널 정보 행렬

에 대해, 우선순위가 높은 사용자일수록 k의 낮은 인덱스로 재정렬한 후, LQ 분해를 적용하여 프리코딩 행렬을 계산한다. 즉, 상기

에 대해 LQ 분해를 통해 하삼각행렬

과 유니터리(unitary) 행렬

을 획득하고, 상기 획득된

의 허미션(hermitian)인

를 송신 선처리 행렬로서 계산한다. 여기서, 사용자간 우선순위를 결정하는 방법은 목적 함수에 따라 변형이 가능하며, 전체 전송 용량의 증대가 목적인 경우 그램-슈미트(Gram-Schmidt) 정렬 방식을 사용하고 사용자간 공평성이 목적인 경우 비례적 공평성(Proportional Fairness : PF)을 통해 우선순위를 결정할 수 있다. On the other hand, in step 409, the calculated power allocation coefficient vector

If all or some of the elements are not positive, the central control unit allocates power to the transmission data according to a general power allocation algorithm in step 427, and in step 429

Channel information matrix

For, the higher priority user is rearranged to a lower index of k, and then LQ decomposition is applied to calculate the precoding matrix. That is

Lower triangular matrix through LQ decomposition for

And unitary matrices

To obtain the obtained

Hermitian of

Is computed as the transmit preprocessing matrix. Here, the method of determining the priority among users can be modified according to the objective function, and if the purpose is to increase the total transmission capacity, use the Gram-Schmidt alignment method and the proportional fairness when the fairness between users is the purpose. (Proportional Fairness: PF) helps determine priorities.

만 포함된 형태인

로 나타나게 된다.In step 431, the central control unit performs precoding on the transmission data using the calculated precoding matrix. In step 425, the central control unit transmits the precoded data. Thus, the received signal at each terminal is a lower triangular matrix

Included only

Will appear.

The central control unit then terminates the algorithm according to the invention.

5 to 7 are graphs showing simulation results for performance evaluation of the scheme proposed in the present invention. In this simulation, a terminal is randomly generated in each sector area under a sectorized cell structure in which M = K = 3, and a distance between base stations is 500m, a path loss index of 3.76, and a flat fading channel. A model was used and a total of 24 external base stations were tested in a 2-tier configuration.

First, FIG. 5 is a graph illustrating a Lagrange multiplier for an iterative index in an optimal power allocation and ZF with ZF beamforming method according to an embodiment of the present invention. Referring to FIG. 5, it can be seen that after about 10 iterations, the Lagrange multiplier value for each base station converges to an arbitrary positive number. Through this, we can confirm that complementary slackness theory holds among KKT conditions that power constraint inequality holds as an equation.

FIG. 6 is a graph illustrating a repetitive index of transmission power at each base station in an optimal power allocation and ZF beamforming scheme according to an embodiment of the present invention. Referring to FIG. 6, as in FIG. 5, it can be seen that after about 10 iterations, transmission powers of all base stations converge to a normalized value 1.

7 is a graph showing the performance between the transmission scheme proposed in the present invention and the conventional transmission scheme as a distribution function of transmission capacity per base station.

로 전송을 하므로 최대의 간섭 신호를 받는다는 특징이 있는 반면, 전송하고자 하는 신호 성분 역시 항상 최대 전력

전송을 하므로 단말이 지리적으로 기지국에 가까이 위치할수록 큰 전송 용량을 얻게 된다. Referring to FIG. 7, the conventional SAT method is a single antenna transmission method, and since each base station transmits a single stream to a terminal geographically closest to each terminal, each terminal transmits an interference signal of the same size from signals transmitted to other terminals. Receive. In other words, the SAT method means that each base station always has an average power.

It is characterized by receiving the maximum interference signal because it transmits by

Since the terminal is geographically located closer to the base station, a larger transmission capacity is obtained.

를 모두 동일한 값으로 할당하는 방식이다.Conventional ZF with equal power method is a conventional general ZF beamforming method,

This method assigns all to the same value.

Conventional ZF-DPC method is a non-linear preprocessing method, and is known as a method of showing high transmission capacity performance by removing interference signals between terminals by a dirty paper encoding and decoding process compared to linear preprocessing methods such as the SAT method and the ZF with equal power method. have. Specifically, when the LQ decomposition scheme includes only the components of the lower triangular matrix L in the transmission signal, the ZF-DPC scheme further eliminates interference between users through dirty paper encoding, thereby non-diagonal (L- diagonal elements are further canceled out.

It can be seen that the ZF with power boost method and the LQ decomposition method according to the present invention can obtain a higher transmission capacity performance than the SAT method and the ZF with equal power method. In particular, the ZF with power boost method has a high transmission capacity. It can be seen that it shows superior performance in the region, and that the LQ decomposition scheme shows superior performance in the low transmission capacity region. In addition, when the multimode using the ZF with power boost method and the LQ decomposition method is selectively applied, as in the embodiment of the present invention, rather than performing the ZF with power boost method and the LQ decomposition method, respectively. In addition, the performance shows a difference in transmission capacity within about 0.5bps / Hz / BS even in the low transmission region of the cumulative distribution function compared to the ZF-DPC performance. In this case, 54% of the ZF beamforming method and 46% of the LQ decomposition method were used in the above experiment. In addition, when the multi-mode is applied, it can be seen that it is superior to each individual performance curve.

In the meantime, the present invention has been described with an example of an optimal multi-user ZF beamforming scheme for maximizing transmission capacity and an algorithm for selectively using LQ decomposition scheme under the maximum power constraint per base station. It is also possible to selectively use the method with the highest transmission capacity, including uncooperative transmission.

Meanwhile, in the detailed description of the present invention, specific embodiments have been described, but various modifications are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined not only by the scope of the following claims, but also by the equivalents of the claims.

1 is a diagram showing the structure of a distributed MIMO system according to the present invention;

2 illustrates another structure of a distributed MIMO system according to the present invention;

3 is a block diagram showing an apparatus for optimizing a transmission power of a central control unit in a distributed MIMO system according to an embodiment of the present invention;

4 is a flowchart illustrating a transmission power optimization method of a central control unit in a distributed MIMO system according to an embodiment of the present invention;

5 is a graph showing a Lagrange multiplier for a repetitive index in an optimal power allocation and ZF with power boost scheme according to an embodiment of the present invention;

FIG. 6 is a graph illustrating a repetitive index of transmission power at each base station in an optimal power allocation and ZF with power boost scheme according to an embodiment of the present invention; and

7 is a graph showing the performance between the transmission scheme proposed in the present invention and the conventional transmission scheme as a distribution function of transmission capacity per base station.

Claims (16)

A method for transmitting data in a multiple input multiple output (MIMO) system in which geographically distributed base stations are connected by wire or dedicated line, Allocating power to transmission data in consideration of a maximum power requirement per base station; And performing zero-forcing (ZF) beamforming on the transmission data to which the power is allocated. The method of claim 1, The power is allocated to satisfy the objective equation and conditional expression of Equation 9 below.

Here, K means the number of terminals, and M means the number of base stations. remind

Is a power normalization coefficient, which is a power allocation optimization variable for satisfying a maximum power constraint per base station through power allocation.

Is the interference signal power value at terminal k. remind

Is a transmission preprocessing matrix, obtained as a pseudo inverse of the channel matrix,

Is the maximum power that can be transmitted per base station. The method of claim 2, wherein the power allocation process, Defining a function of a Lagrange multiplier for power allocation optimization variables by applying a Lagrange function to the objective and conditional expressions, Initializing Lagrange multipliers for all base stations; Updating the Lagrange multiplier value until the Lagrange multiplier value for all base stations converges to an arbitrary value, Calculating a power allocation optimization variable by applying the converged Lagrange multiplier value to a function of the defined Lagrange multiplier when the Lagrange multiplier values for all base stations converge to an arbitrary value; And allocating power to transmission data by applying the power allocation optimization variable. The method of claim 3, wherein The Lagrange multiplier function for the power allocation optimization variable is defined as Equation 10 below.

Where

Is the Lagrange multiplier vector. The method of claim 3, wherein The Lagrange multiplier value is updated using Equation (11).

Where

Is the Lagrange multiplier vector, and

Is any positive constant. remind

N is the repetition index for the update. The method of claim 1, Receiving feedback of the channel information matrix from the terminals; Calculating a pseudo inverse of the channel information matrix; Calculating a power allocation coefficient vector using the calculated pseudo inverse matrix; Checking whether all elements of the calculated power allocation coefficient vector are positive; The power allocation process and the ZF beamforming process considering the maximum power condition per base station are performed when all elements of the calculated power allocation coefficient vector are positive. The method of claim 6, wherein the power allocation coefficient vector calculation process, Calculating a matrix having power values of the elements of the pseudo inverse matrix as elements; Calculating the power allocation coefficient vector by multiplying the calculated inverse of the matrix by a maximum power vector. The method of claim 6, Obtaining a unitary matrix by applying LQ decomposition to the channel information matrix when all elements of the calculated power allocation coefficient vector are negative; Calculating a precoding matrix using the obtained unitary matrix's hermition; And precoding the transmission data by using the calculated precoding matrix. An apparatus for transmitting data in a multiple input multiple output (MIMO) system in which geographically distributed base stations are connected by wire or dedicated line, A power allocator for allocating power to transmission data in consideration of a maximum power condition per base station; And a precoder for performing transmission by performing zero-forcing (ZF) beamforming on the power-assigned transmission data. The method of claim 9, The power is allocated to satisfy the objective equation and conditional expression of Equation 12 below.

Here, K means the number of terminals, and M means the number of base stations. remind

Is a power normalization coefficient, which is a power allocation optimization variable for satisfying a maximum power constraint per base station through power allocation.

Is the interference signal power value at terminal k. remind

Is a transmission preprocessing matrix, obtained as a pseudo inverse of the channel matrix,

Is the maximum power that can be transmitted per base station. The method of claim 10, wherein the power allocation unit, Means for defining a Lagrange multiplier function for a power allocation optimization variable by applying a Lagrange function to the objective and conditional expressions; Means for initializing a Lagrange multiplier value for all base stations; Means for updating the Lagrange multiplier value until the Lagrange multiplier value for all base stations converges to an arbitrary value, Means for calculating a power allocation optimization variable by applying the converged Lagrange multiplier value to a function of the defined Lagrange multiplier when the Lagrange multiplier values for all base stations converge to an arbitrary value; Means for assigning power to transmission data by applying the power allocation optimization variable. The method of claim 11, The Lagrange multiplier function for the power allocation optimization variable is defined as Equation (13).

Where

Is the Lagrange multiplier vector. The method of claim 11, The Lagrange multiplier value is updated using Equation (14).

Where

Is the Lagrange multiplier vector, and

Is any positive constant. remind

N is the repetition index for the update. The method of claim 9, A channel information matrix feedback receiver for receiving feedback of the channel information matrix from the terminals; Compute a pseudo inverse of the channel information matrix, calculate a power allocation coefficient vector using the calculated pseudo inverse matrix, check whether all elements of the calculated power allocation coefficient vector are positive, and calculate And all transmission element coefficients are positive, and further comprising a transmission scheme determination unit for determining performance of power allocation and ZF beamforming in consideration of the maximum power condition per base station. The method of claim 14, wherein the transmission method determination unit,  And calculating a matrix having power values of the elements of the pseudo inverse matrix as elements, and multiplying the calculated inverse matrix by a maximum power vector to calculate the power allocation coefficient vector. The method of claim 14, wherein the transmission method determination unit, When all the elements of the calculated power allocation coefficient vector are negative, LQ decomposition is applied to the channel information matrix to obtain a unitary matrix, and a precoding matrix is obtained by using the obtained unitary matrix. And after calculating, precoding the transmission data using the calculated precoding matrix and transmitting the precoding data.

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