KR20220146928A - Method for beamforming and power allocation in cell-free MIMO system, and apparatus therefor - Google Patents

Abstract

A beamforming and power allocation method performed in a cell-free MIMO system may comprise the steps of: determining an uplink reception beamforming vector for each user terminal in a central processing unit (CPU); determining a downlink transmission beamforming vector for each user terminal in the CPU; and allowing, by the CPU, a plurality of access points (APs) to perform uplink reception using the uplink reception beamforming vector and the plurality of APs to perform downlink transmission using the downlink transmission beamforming vector. Accordingly, a beamforming and power allocation method is provided to provide a constant transmission rate to all users without decreasing the transmission rate even when the number of users increases.

Description

Method for beamforming and power allocation in cell-free MIMO system, and apparatus therefor

The present invention relates to a communication technology applicable to a cell-free MIMO system, and more particularly, a beam for maximizing communication performance while guaranteeing fair performance to users in a cell-free MIMO system. It relates to a forming and power allocation method and an apparatus therefor.

Multi-antenna technology has been discussed as a core technology for improving performance in wireless communication systems for the past 50 years. Among the multiple antenna technologies, the most important technique is to simultaneously transmit multiple data streams on the same time and frequency resource using multiple antennas. In order to efficiently transmit and receive multiple data streams, it is necessary to maximize magnetic signal power and minimize interference signal power by utilizing channel information between transceivers. In order to maximize the magnetic signal power, a maximum ratio combining (MRC) method may be used as a receive beamforming method, and a maximum ratio transmission (MRT) method may be used as a transmit beamforming method. In order to minimize interference signal power, a zero-forcing (ZF) method may be used as a transmission/reception beamforming method. In addition, a minimum mean square estimation (MMSE) method may be used as a method of maximizing a signal to interference and noise (SINR) value in consideration of signal power and interference at the same time.

Multi-antenna technology is a mature field that has been studied over a long period of time, and in order to satisfy various purposes, it provides equity by maximizing the sum of the transmission rates for each user, minimizing the transmission power, or providing the same transmission rate for each user depending on the service purpose. has been modeled as an optimization problem, and a cell-free MIMO system that can provide good performance even for cell boundary users has been recently proposed.

SUMMARY OF THE INVENTION It is an object of the present invention to solve the above problems, to provide a beamforming and power sharing method performed in a cell-free MIMO system.

Another object of the present invention to solve the above problems is to provide a cell-free MIMO system including a central processing unit (CPU) and a plurality of access points (APs) for performing the method. is doing

An embodiment of the present invention for achieving the above object is a beamforming and power allocation method performed in a cell-free MIMO system composed of a central processing unit (CPU) and a plurality of access points (APs). determining an uplink reception beamforming vector for each user terminal in the CPU; determining, in the CPU, a downlink transmission beamforming vector for each user terminal; and and causing, by the CPU, a plurality of APs to perform uplink reception using the uplink reception beamforming vector, and allowing the plurality of APs to perform downlink transmission using the downlink transmission beamforming vector. can do.

The uplink reception beamforming vector may be determined using a maximum ratio combining (MRC) method.

In order to receive the received signals from all user terminals with the same power in the uplink of the cell-free MIMO system, the transmit power of the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. can

Signal to integrity and noise ratio (SINR) values of signals of user terminals in the uplink of the cell-free MIMO system may have the same value.

The downlink reception beamforming vector may be determined using a maximum ratio transmission (MRT) method.

In order to allow all user terminals to receive a signal with the same power in the downlink of the cell-free MIMO system, the transmit power allocated to the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. can

SINR values of signals transmitted to user terminals in the downlink of the cell-off MIMO system may not have the same value.

The uplink reception beamforming vector may be determined using a zero forcing (ZF) method.

The optimal beamforming vector of the ZF method is selected from a left generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals.

The downlink transmission beamforming vector may be determined using a ZF method.

The optimal beamforming vector of the ZF method is selected from a right generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals.

An embodiment of the present invention for achieving the above another object is a cell-breaking MIMO system composed of a CPU and a plurality of APs, wherein an uplink reception beamforming vector for each user terminal and a downlink transmission beamforming vector for each user terminal are provided. a CPU that determines and transmits to the plurality of APs; and It may include a plurality of APs that perform uplink reception using the uplink reception beamforming vector and perform downlink transmission using the downlink transmission beamforming vector.

The uplink reception beamforming vector may be determined using a maximum ratio combining (MRC) method.

In order to receive the received signals from all user terminals with the same power in the uplink of the cell-free MIMO system, the transmit power of the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. can

The downlink reception beamforming vector may be determined using a maximum ratio transmission (MRT) method.

In order to allow all user terminals to receive a signal with the same power in the downlink of the cell-free MIMO system, the transmit power allocated to the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. can

The uplink reception beamforming vector may be determined using a zero forcing (ZF) method.

The optimal beamforming vector of the ZF method is selected from a left generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals.

The downlink transmission beamforming vector may be determined using a ZF method.

The optimal beamforming vector of the ZF method is selected from a right generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals.

There are two main factors that make it difficult to achieve high frequency efficiency for all users in a cell. The first factor originates from cell-boundary users. Cell boundary users receive their signals at a low signal level due to path loss, and interference signals from neighboring cells are strongly introduced, thereby reducing signal to interference and noise (SINR) determining frequency efficiency. The second factor is due to a decrease in the transmission rate per user as the number of users in the cell increases. In order to avoid interference, users in a cell divide and use radio resources so that they do not overlap, and as the number of users increases, the allocated radio resources decrease and the transmission rate per user decreases.

Cell-free MIMO is a system that enables simultaneous transmission of multiple data using the same radio resource as the received signal strength improvement by shortening the transmission distance between transceivers in order to solve the threat of achieving performance indicators due to cell boundary users and increased users to be. Embodiments of the present invention provide a beamforming and power allocation method for providing high performance to cell-edge users in a cell-breaking MIMO system, and providing a constant data rate to all users without reducing the data rate even if the number of users increases.

1 is a conceptual diagram for explaining a problem of a decrease in capacity per user according to the number of users and a goal of a beamforming and power allocation method according to embodiments of the present invention.
2 is a conceptual diagram for explaining a conventional network MIMO system and a cell-free MIMO system.
3 is a conceptual diagram for explaining a method for optimizing uplink/downlink signal beamforming and strategy allocation in a cell-off MIMO system according to an embodiment of the present invention.
4 is a conceptual diagram for explaining a method for optimizing uplink/downlink interference beamforming and power allocation in an out-of-cell MIMO system according to an embodiment of the present invention.
5 is a flowchart for explaining a method of operating an out-of-cell MIMO system according to an embodiment of the present invention.
6 is a block diagram illustrating the configuration of a communication node according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. The term "and/or" includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it is understood that other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It is to be understood that this does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

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

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram for explaining a problem of a decrease in capacity per user according to the number of users and a goal of a beamforming and power allocation method according to embodiments of the present invention.

Referring to FIG. 1 , mobile communication technologies, including 5G, have dramatically increased the total system capacity through bandwidth expansion, frequency efficiency improvement, base station density increase, and the like. However, there is still a limit to increasing the data rate of cell edge users, and since the total system capacity is divided and used by multiple users, the data rate per user (terminal) decreases as the number of users (user terminals) increases. are doing

Embodiments of the present invention are intended to provide a technology capable of providing constant performance to all users in a cell by overcoming the above-described problem. In order to provide a constant data rate to users, it is necessary to provide a high data rate to cell-edge users, and to solve the problem of a data rate decrease due to an increase in users (user terminals). To realize this, data must be transmitted to multiple users simultaneously using the same radio resource using multiple antennas, and it is essential to overcome interference between users. If interference between users can be efficiently controlled by using multiple antennas, the transmission rate can be increased in proportion to the increase in the number of antennas. In addition, if the distance between transmission and reception is shortened by installing antennas everywhere, it will be possible to provide a high transmission rate to cell-edge users. Embodiments of the present invention aim to provide high performance to cell-edge users through a transformative technology differentiated from existing mobile communication technologies, and to provide a constant data rate to all users without reducing the data rate even if the number of users increases.

Embodiments of the present invention propose various beamforming and power allocation methods applicable to a cell-free MIMO system. The cell-free MIMO system can be interpreted as an advanced system in which the existing MU-MIMO system distributes a plurality of antennas.

2 is a conceptual diagram for explaining a conventional network MIMO system and a cell-free MIMO system.

The conventional network MIMO system shown in (a) of FIG. 2 has a configuration in which each of a plurality of base stations (BS) is connected to a plurality of distributed antennas to control the connected distributed antennas. On the other hand, in the cell-free MIMO system shown in FIG. It has a configuration that is controlled by being connected to a CPU, central processing unit). In particular, a system in which a large number of APs participate to provide a massive MIMO service to a terminal is defined as a cell-free massive MIMO (CFmMIMO) system.

The cell-free MIMO system is a system for providing the same performance to all users by installing multiple APs in various places in a wide area, and can be viewed as one of distributed antenna technologies. Despite the theoretical advantages of the existing distributed antenna system, cooperative multi-point transmission, and network MIMO technology, after 5G, one by one, difficulties in actual implementation due to network synchronization problems and fronthaul capacity limitations are overcome. It is attracting attention as a major technology. Unlike the existing cell-based distributed antenna technology, the CFmMIMO system operates in a TDD method to acquire uplink and downlink channel information, and is a structure in which APs distributed over a wide area connected to the CPU through a fronthaul transmit and receive data to a small number of users at the same time. If a lot of APs are installed, channel hardening effect can be obtained, so optimal performance can be achieved with a relatively simple signal processing technique, and the same performance can be provided to all users.

Hereinafter, 'cell-breaking MIMO system' is used interchangeably with 'CFmMIMO system' to have the same meaning.

system model

Hereinafter, a system model of a CFmMIMO system applied to embodiments of the present invention is described.

In a cell-free MIMO environment, a plurality of access points (APs) are distributed and installed in space, and each AP may have one antenna. Using these, a service can be provided to multiple user terminals each having one antenna. In such an environment, there are restrictions on the transmission power of each user terminal in the uplink and the transmission power of each AP in the downlink. In addition, when the user terminal does not decode the interference signal or uses one antenna, it is difficult to control each other between the transmission signals in the uplink and it is difficult to control the reception signals in the downlink. Accordingly, in the uplink, appropriate interference control is required through receive beamforming performed by the AP, and in downlink, appropriate interference control is required through transmit beamforming performed by the AP. In addition, in the uplink, power allocation for each user (power allocation) for each user, in the downlink, a power allocation method for each AP is required. In order to determine the optimal beamforming method, mathematical modeling is required. Symbols used in the following equations are defined as follows.

의 전력으로 하기 수학식 1로 표현되는 신호를 송신하게 된다.In a cell-apart MIMO environment in which K user terminals each having one antenna and L APs each having one antenna exist, uplink transmission beamforming is not used, and each user terminal is

A signal expressed by Equation 1 below is transmitted with a power of .

를 전송하기 위한 전력 할당

는

로 정의되며,

이다.Here, the signal

power allocation to transmit

Is

is defined as

to be.

Signals received from K user terminals to L APs through channels may be expressed as Equation 2 below.

)을 적용하여 수신되는 신호는 하기 수학식 3과 같이 표현될 수 있다.Receive beamforming at the AP (

A signal received by applying ) may be expressed as Equation 3 below.

The average received signal power in the uplink may be expressed as in Equation 4 below.

In addition, the uplink average noise power may be expressed as in Equation 5 below.

The uplink received signal power and uplink interference power for user i can be expressed as in Equation 6 below.

Accordingly, the SINR value of the uplink i-th user may be expressed as in Equation 7 below.

In a cell-apart MIMO environment in which K user terminals each having one antenna and L APs each having one antenna exist, a downlink transmission signal through transmission beamforming may be expressed as Equation 8 below. .

의 L2 norm은 1이며, 사용자별 송신 빔포밍 벡터에 해당하는 각 열벡터

의 L2 norm은 1이다. 셀-탈피 MIMO 환경에서, 하향링크에는 하기 수학식 9로 표현되는 것과 같이 AP 별 전력 할당에 제한이 있다고 가정한다. Here, Q denotes a power allocation matrix for each AP, and P denotes a power allocation matrix for each user. Each row vector corresponding to the transmit beamforming vector for each AP

L2 norm of is 1, and each column vector corresponding to a transmission beamforming vector for each user

The L2 norm of is 1. In a cell-free MIMO environment, it is assumed that downlink has a limitation in power allocation for each AP as expressed by Equation 9 below.

A signal received to user terminals through a channel may be expressed as in Equation 10 below.

)이 도입된 이유는 각 사용자 단말의 수신 신호를 열을 기준으로 스칼라 값이 되도록 표현하기 위해서이다. 각 사용자 단말은 수신 빔포밍 없이 신호를 수신하게 되고, 이를 다시 정리하면 하기 수학식 11과 같이 표현될 수 있다.In Equation 10, the conjugate transpose matrix of H instead of H

) is introduced to express the received signal of each user terminal as a scalar value based on a column. Each user terminal receives a signal without receiving beamforming, and if this is rearranged, it can be expressed as Equation 11 below.

The average received signal power in the downlink may be expressed as Equation 12 or 13 below.

The average noise power in the downlink can be expressed as Equation 14 below.

The downlink reception signal power and downlink interference power in the user terminal may be expressed as in Equation 15 below.

Accordingly, the SINR value of the i-th user terminal in the downlink may be expressed as in Equation 16 below.

Restriction conditions for the uplink beamforming vector and the downlink beamforming vector in the cell-out of the MIMO system may be organized as follows.

In addition, restrictions on uplink transmission power and downlink transmission power in the cell-free MIMO system may be summarized as follows.

Signal Optimization (Maximization) Beamforming and Power Allocation Methods

In the cell-total MIMO system, a beamforming and power allocation method for optimizing a signal for each user terminal (in particular, a signal received from each user terminal while maximizing a received signal as a way to provide the same performance to user terminals) A method of making the size of the same) is presented.

In the cell-free MIMO system, the uplink received signal power for user terminal i may be expressed as Equation 17 below.

In the cell-free MIMO system, the downlink reception signal power for the user terminal i may be expressed as Equation 18 below.

The optimization problem of maximizing the uplink received signal can be modeled as in Equation 19 below.

In an optimization problem of maximizing an uplink received signal, signals of user terminals do not affect each other. In addition, since the vector direction does not change even if the vector is multiplied by a scalar value, the optimization problem is not affected even if the transmission signal and reception beamforming method for each user terminal are separately considered. That is, the uplink transmission power and the beamforming method for each user may be independently applied without considering other user terminals. Additionally, in order to provide the same performance in cell-free MIMO, the received power for each user is made the same.

As a result, it becomes an eigenvalue problem like the problem presented in the point-to-point multi-antenna, and the uplink optimal reception beamforming vector maximizing the uplink reception signal power can be derived as shown in Equation 20 below in the MRC method.

가 되며, 모든 사용자 단말들로부터의 수신 신호가 동일한 전력으로 수신되도록 하기 위한 전력 할당 방법은 하기 수학식 21과 같이 표현될 수 있다.The signal power received by the MRC beamforming method is

, and a power allocation method for allowing received signals from all user terminals to be received with the same power can be expressed as Equation 21 below.

로 정해진다. 결과적으로 각 사용자 단말의 채널의 크기 값에 반비례하여 전력을 할당해야 하는 결론에 도달한다. Here, the signal strength received with the same value for all user terminals is

is determined by As a result, we arrive at the conclusion that power should be allocated in inverse proportion to the channel size value of each user terminal.

Since the interference signal is also received in the uplink of the cell-free MIMO system, the interference signal for each user terminal can be expressed as Equation 22 below.

When a beamforming method for maximizing a received signal in uplink of a cell-free MIMO system is applied and a power allocation method for equalizing received signal power is applied, the SINR value for each user terminal is the same as expressed by Equation 23 below. will have a value

The optimization problem of maximizing the received signal in the downlink can be modeled as in Equation 24 below.

The optimization problem of maximizing the downlink reception signal does not affect the optimization problem even if the uplink transmission signal and transmission beamforming method for each AP are separately considered.

An optimal transmit beamforming vector for maximizing receive power in downlink of a cell-free MIMO system may be expressed as Equation 25 below in the MRT method.

In order to ensure that the received power of the signals transmitted by the MRT beamforming method are the same, the condition expressed by Equation 26 below must be satisfied.

Transmission power to which MRT beamforming is applied is expressed by Equation 27 below.

In order to satisfy the transmission power constraint for each AP, the condition expressed by Equation 28 below must be satisfied.

Accordingly, downlink reception power satisfying the transmission power constraint condition can be obtained as shown in Equation 29 below.

As a result, the power allocated to each user terminal is inversely proportional to the channel size value of each user terminal and can be expressed as Equation 30 below.

The transmit power of each AP is multiplied by P and G obtained above, and corresponds to the L2 norm of the column direction vector.

When there is a transmission power constraint per AP in the cell-free MIMO downlink, and a beamforming method and power allocation that maximizes reception power and makes the same for all user terminals are applied, the received power for each user terminal is calculated by the following equation 31 can be obtained.

In the downlink, the interference power for each user terminal is not the same as in the uplink, and can be obtained as shown in Equation 32 below.

A received SINR value according to a method of optimizing received power in downlink of a cell-free MIMO system may be expressed as Equation 33 below.

3 is a conceptual diagram for explaining a method for optimizing uplink/downlink signal beamforming and strategy allocation in a cell-off MIMO system according to an embodiment of the present invention.

(수학식 25)으로 정리되며, 각 사용자 단말에서의 수신 전력은

(수학식 31)로 정리되며, 사용자 단말별 전력 할당은

(수학식 30)으로 정리된다.Referring to FIG. 3 , a downlink transmission beamforming vector for optimizing (maximizing) a downlink signal is

(Equation 25), and the received power at each user terminal is

(Equation 31), and the power allocation for each user terminal is

(Equation 30).

(수학식 20)으로 정리되며, 사용자 단말 별 수신 전력은

으로 정리되며, 각 사용자 단말별 전력 할당은

(수학식 21)로 정리된다.In addition, the uplink transmission beamforming vector for optimizing (maximizing) the uplink signal is

(Equation 20), and the received power for each user terminal is

, and the power allocation for each user terminal is

(Equation 21).

A reception beamforming method for maximizing a reception signal in the uplink of a cell-free MIMO system is an MRC method, and transmission power for each user terminal should be allocated in inverse proportion to a channel size value. As a result, SINR values of signals of user terminals in uplink have the same value. In the downlink of the cell-free MIMO system, the transmission beamforming method for maximizing the received signal is the MRT method, and power in inverse proportion to the channel size value is allocated to each user terminal, and the transmit power per AP is inversely proportional to the channel size value. not decided In addition, the SINR values of user terminals are not the same in downlink of the cell-off MIMO system, and a signal is transmitted with a smaller SINR value to a user terminal having a poor channel. In the downlink of the cell-apart MIMO system, the problem that the same SINR value for each user terminal is not achieved through a signal optimization (maximization) method can be solved through an interference optimization method to be described later.

Interference Optimization Beamforming and Power Allocation Method

A beamforming and power allocation method for optimizing interference for each user terminal (in particular, in order to provide the same performance to the user terminals, the interference power is set to a minimum value (ie, 0), and the SINR values for the user terminals are the same. how to do it) is presented.

In the uplink of the cell-free MIMO system, the received interference power for each user terminal may be expressed as Equation 34 below.

In the downlink of the cell-free MIMO system, the received interference power for each user terminal may be expressed as Equation 35 below.

The optimization problem of minimizing interference in the uplink can be modeled as in Equation 36 below.

In the cell-apart MIMO system, since only the case where the number of APs is greater than the number of user terminals is considered, the dimension of the left null space of the channel is always greater than or equal to 1, and a zero-forcing (ZF) beamforming method that always makes the interference equal to 0 is applied. can In general, since the dimension of the left null space is much greater than 1, in addition to the ZF method in which the interference is set to 0, an optimization problem of maximizing the minimum SINR value of the signal received from the user terminals modeled as in Equation 37 below is additionally applied. can

로서 두 스칼라 값의 곱으로 동일하게 표현될 수 있기 때문에 독립적으로 최적화가 수행될 수 있다. 사용자 단말들로부터 수신되는 신호의 최소 SINR값을 최대화하는 최적화 문제는, 결과적으로는 동일한 SINR값이 되도록 하는 빔포밍과 전력 할당 값을 최적 해로 도출하게 된다. 즉, 최적 값에서

를 만족하게 된다.In this additional optimization problem, since the interference is made to be 0, there is no influence between the user terminals, and the power of each user terminal and the signal power received by beamforming are also

Since it can be equally expressed as a product of two scalar values, optimization can be performed independently. In the optimization problem of maximizing the minimum SINR value of the signal received from the user terminals, beamforming and power allocation values that result in the same SINR value are derived as an optimal solution. That is, at the optimal value

will be satisfied with

That is, the optimization problem of maximizing the minimum SINR value of a signal received from user terminals can be transformed into the same problem as in Equation 38 below.

A left pseudo inverse of the channel matrix is used as a ZF beamforming method with zero interference. When a pseudo inverse is used, a vector having a minimum L2 norm is derived as a solution, and thus, it can be an optimal solution from the viewpoint of minimizing the sum of transmit powers of user terminals. However, in the uplink of the cell-breaking MIMO system, the sum of the transmit powers of the user terminals is not a constraint, but the transmit power for each user terminal is a constraint. In this case, the beamforming method using the pseudo inverse matrix does not become an optimal solution, and an optimal beamforming vector must be selected from a generalized inverse (particularly, a left generalized inverse). The left general inverse matrix of the channel matrix to be applied to the uplink beamforming method of the cell-apart MIMO system has the form of Equation 39 below, and when it is not a square matrix, it can be given in countless forms according to the U matrix.

Reception beamforming performing ZF with transmission power constraint for each user terminal may have the form of Equation 40 below.

The optimization problem to which the left general inverse matrix is applied can be transformed as shown in Equation 41 below.

Here, if S is a conjugate transpose matrix, and SP is also an unrelated transpose matrix, the property that the commutative law holds with S, that is, PS = SP, is used.

The fact that the two matrices in the constraint condition are the same means that each column can be transformed into the same optimization problem as shown in Equation 42 below.

Since each column is the same, the L2 norm is also the same, so the optimization problem is the same as the optimization problem expressed by Equation 43 below.

If one constraint condition for the reception beamforming vector norm value is applied, it may become an optimization problem expressed by Equation 44 below.

The optimization problem expressed by combining the objective function and the constraint can be expressed as Equation 45 below.

Accordingly, the optimal SINR value can be expressed as Equation 46 below.

When the transmit power constraint conditions of all user terminals are the same, Equation 46 may be expressed as Equation 47 below.

Here, U is a solution satisfying Equation 48 below.

과

를 구하면, 하기 수학식 49를 통해 셀-탈피 MIMO 상향링크 최적 수신 빔포밍 벡터와 사용자별 전력 할당이 도출될 수 있다.The problem of finding U is a convex problem with a second-order cone program, and an easy way to find a solution through a software package is known. therefore optimal

class

, a cell-apart MIMO uplink optimal reception beamforming vector and power allocation for each user can be derived through Equation 49 below.

Accordingly, the uplink optimal reception beamforming vector may be expressed as Equation 50 below.

Signal power received in the uplink may be expressed as in Equation 51 below.

Accordingly, the optimal power allocation for each user terminal can be expressed as Equation 52 below.

If the optimal beamforming method and the optimal power allocation method derived from the cell-apart MIMO system are applied, the interference signal for each user terminal becomes 0, the SINR value becomes the same, and as a result, the received signal becomes the same. have.

The optimization problem of minimizing interference in downlink can be modeled as in Equation 53 below.

In the uplink, there is a user terminal transmit power constraint, whereas in the downlink, there is a transmit power constraint for each AP. Considering the difference in constraint conditions, the process of deployment in the uplink should be similarly followed and arranged.

In the downlink, as in the uplink, only the case where the number of APs is greater than the number of user terminals is considered. In addition to ZF with zero interference, the minimum SINR value received by each user terminal modeled as in Equation 54 below is maximized. An optimization problem may additionally be applied.

For the same reason as the uplink, the optimization problem of maximizing the received minimum SINR value can be transformed into the same problem expressed by Equation 55 below.

를 만족하기 위해 좌 일반 역행렬 로 수신 빔포밍 행렬을 구하였지만, 하향링크에서는

를 만족하는 송신 빔포밍 행렬을 구하기 위해,

의 우 일반 역행렬(right generalized inverse)을 이용한다.

의 우 일반 역행렬은 수학식 56과 같은 형태를 가질 수 있다.It is expressed as an optimization problem similar to that of uplink, but there is a difference in the form of a general inverse matrix of a channel matrix for obtaining a transmit beamforming matrix. That is, in the uplink

The reception beamforming matrix was obtained with the left general inverse matrix to satisfy

In order to obtain a transmission beamforming matrix that satisfies

A right generalized inverse is used.

The general inverse matrix may have the form of Equation 56.

Transmission beamforming performing ZF with transmission power constraint for each AP may have the form of Equation 57 below.

를 만족하며, U가 임의의 행렬이기 때문에 G는 유일한 행렬로서 주어지지 않으며, 최적 G가 결정되어야 한다. Here, since the projection matrix is a Hermitian matrix,

, and since U is an arbitrary matrix, G is not given as a unique matrix, and an optimal G must be determined.

위치 변경이 가능하다는 성질을 이용하면, 최적화 문제는 하기 수학식 58과 같이 변형될 수 있다.Apply the right generalized inverse, diagonal matrix

Using the property that the position can be changed, the optimization problem can be transformed as shown in Equation 58 below.

Here, if S is a conjugate transpose matrix, and SP is also an unrelated transpose matrix, the property that the commutative law holds with S, that is, PS = SP, is used.

The fact that the two matrices in the constraint condition are the same means that each column can be transformed into the same optimization problem as shown in Equation 59 below.

Since the downlink is multiplied from the right unlike the uplink, each row of the G matrix is multiplied by each element of the diagonal matrix. On the other hand, since the constraint is given by each row instead of by each column, it is expressed in a different form from that of the uplink.

Since each column is the same, the L2 norm is also the same, so the optimization problem is the same as the optimization problem expressed by Equation 60 below.

If one constraint condition for the transmit beamforming vector norm value is applied, it may become an optimization problem expressed by Equation 61 below.

The optimization problem expressed by combining the objective function and the constraint can be expressed as Equation 62 below.

Therefore, the optimal SINR value can be expressed as Equation 63 below.

When the constraint condition is the same for the transmit power of all APs, Equation 63 may be expressed as Equation 64 below.

Here, U is a solution satisfying Equation 65 below.

In the downlink, like the uplink, U can be obtained through a second-order cone program, and the cell-free MIMO downlink optimal transmission beamforming vector and power allocation for each user are derived through Equation 66 below. can be

Accordingly, the optimal transmission beamforming vector for each user in the downlink can be expressed as Equation 67 below.

In addition, the optimal transmit beamforming vector for each AP may be expressed as Equation 68 below.

Signal power received in the downlink can be expressed as in Equation 69 below.

Accordingly, the optimal power allocation for each user terminal may be expressed as Equation 70 below.

The optimal power allocation for each AP can be expressed as in Equation 71 below.

As in the uplink, if the optimal beamforming method derived from the cell-off MIMO and the optimal power allocation are applied in the downlink, the interference signal for each user becomes 0, the SINR value becomes the same, and as a result, the received signal becomes the same. results can be obtained.

4 is a conceptual diagram for explaining a method for optimizing uplink/downlink interference beamforming and power allocation in an out-of-cell MIMO system according to an embodiment of the present invention.

(수학식 68) 로 정리되며, AP 별 최적 전력 할당은

(수학식 71)로 정리된다.Referring to FIG. 4 , a downlink transmission beamforming vector for each AP maximizing an SINR value while reducing interference to 0 in downlink is

(Equation 68), and the optimal power allocation for each AP is

(Equation 71).

(수학식 50)으로 정리되며, 사용자 단말 별 최적 전력 할당은

(수학식 52)로 정리된다.In addition, the uplink reception beamforming vector for maximizing the SINR value while reducing the interference to 0 in the uplink is

(Equation 50), and the optimal power allocation for each user terminal is

(Equation 52).

Summary of Optimal Beamforming and Power Allocation Methods of Cell-apart MIMO System

A method of beamforming and power allocation for equalizing received power of user terminals while maximizing a received signal in a cell-free MIMO system is as follows.

A beamforming method for maximizing a received signal in the uplink of a cell-free MIMO system is an MRC method, and transmission power for each user terminal should be allocated in inverse proportion to a channel. The transmit power for each AP corresponds to a column-direction vector L2 norm of a matrix obtained by multiplying an optimal beamforming matrix and a power allocation matrix. Using such a beamforming and power allocation method, SINR values received in uplink have the same value.

A beamforming and power allocation method for making the SINR values of user terminals equal to zero while reducing interference in downlink of a cell-free MIMO system is as follows.

The uplink and downlink optimal beamforming method is derived through a general inverse matrix, not a pseudo inverse matrix of a generally used channel matrix. The pseudo-inverse matrix can obtain a solution in which the L2 norm is the smallest vector, which is optimal from the viewpoint of minimizing the sum of powers of user terminals or APs, but power constraint conditions for each user terminal or each AP like a cell-apart MIMO system. It is not optimal if you have . Therefore, it is necessary to obtain an optimal matrix from among general inverse matrices that satisfy ZF.

5 is a flowchart for explaining a method of operating an out-of-cell MIMO system according to an embodiment of the present invention.

Referring to FIG. 5 , the CPU of the cell-breaking MIMO system sets an uplink reception beamforming vector and a downlink reception beamforming vector to be applied to the APs (AP #1, AP #2, ..., AP #L), respectively. Power allocation for the AP and the user terminals may be determined (S510).

The process of determining the uplink reception beamforming vector, the downlink reception beamforming vector, and the power allocation values in step S510 follows the procedure described above through mathematical modeling, and in FIGS. 3 and 4 , the uplink reception is summarized. A beamforming vector, a downlink reception beamforming vector, and power allocation values may be determined.

On the other hand, although not described in FIG. 5, the CPU receives the channel matrix between each AP and each terminal from the APs (AP #1, AP #2, ..., AP #L), the transmission power constraint of each user terminal, and information such as transmission power constraint conditions of each AP may be collected. A channel matrix between each AP and each terminal may be measured using a pilot or a reference signal transmitted by each AP or each terminal from a counterpart, and the measured results may be aggregated by the CPU.

The CPU may transmit the determined uplink reception beamforming vector, downlink reception beamforming vector, and power allocation values to the APs (AP #1, AP #2, ..., AP #L) (S511, S512, S513).

Thereafter, the APs (AP #1, AP #2, ..., AP #L) use the determined uplink reception beamforming vector, downlink reception beamforming vector, and power allocation values to form cells with the terminals. - It is possible to perform communication based on the escape MIMO (S520).

Meanwhile, the CPU receives the channel matrix between each AP and each terminal from the APs (AP #1, AP #2, ??, AP #L), the transmit power constraint of each user terminal, and the transmit power constraint of each AP. It is possible to re-aggregate information such as, and the uplink reception beamforming vector, the downlink reception beamforming vector, and the power allocation values determined based on the re-collected information to the APs (AP #1, AP #2, .. ., AP #L) can be transmitted again (S531, S532, S533).

Thereafter, the APs (AP #1, AP #2, ..., AP #L) communicate with the terminals using the re-determined uplink reception beamforming vector, the downlink reception beamforming vector, and the power allocation values. Communication can be performed based on cell-free MIMO.

The steps S510 to S530 may be continuously and repeatedly performed.

6 is a block diagram illustrating the configuration of a communication node according to an embodiment of the present invention.

The communication node described with reference to FIG. 6 may be the CPU and/or AP described above. Referring to FIG. 6 , the communication node 600 may include at least one processor 610 , a memory 620 , and a transceiver 630 connected to a network to perform communication. In addition, the communication node 600 may further include an input interface device 640 , an output interface device 650 , a storage device 660 , and the like. Each of the components included in the communication node 600 may be connected by a bus 670 to communicate with each other.

However, each of the components included in the communication node 600 may not be connected to the common bus 670 but to the processor 610 through an individual interface or an individual bus. For example, the processor 610 may be connected to at least one of the memory 620 , the transceiver 630 , the input interface device 640 , the output interface device 650 , and the storage device 660 through a dedicated interface. .

The processor 610 may execute a program command stored in at least one of the memory 620 and the storage device 660 . The processor 610 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed. Each of the memory 620 and the storage device 660 may be configured as at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory 620 may be configured as at least one of a read only memory (ROM) and a random access memory (RAM).

The methods according to the present invention may be implemented in the form of program instructions that can be executed by 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 recorded on the computer readable medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software.

Examples of computer-readable media include hardware devices specially configured to store and carry out program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as at least one software module to perform the operations of the present invention, and vice versa.

Although it has been described with reference to the above embodiments, it will be understood by those skilled in the art that various modifications and changes can be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. will be able

Claims (20)

A method of beamforming and power allocation performed in a cell-free MIMO system composed of a central processing unit (CPU) and a plurality of access points (APs), the method comprising:
determining an uplink reception beamforming vector for each user terminal in the CPU;
determining, in the CPU, a downlink transmission beamforming vector for each user terminal; and
and causing, by the CPU, a plurality of APs to perform uplink reception using the uplink reception beamforming vector, and allowing the plurality of APs to perform downlink transmission using the downlink transmission beamforming vector. doing,
Beamforming and power allocation method. The method according to claim 1,
The uplink reception beamforming vector is determined by a maximum ratio combining (MRC) method,
Beamforming and power allocation method. 3. The method according to claim 2,
In order to receive the received signals from all user terminals with the same power in the uplink of the cell-free MIMO system, the transmit power of the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. ,
Beamforming and power allocation method. 4. The method of claim 3,
Signal to integrity and noise ratio (SINR) values of signals of user terminals in the uplink of the cell-breaking MIMO system have the same value,
Beamforming and power allocation method. The method according to claim 1,
The downlink reception beamforming vector is determined by MRT (maxium ratio transmission) method,
Beamforming and power allocation method. 6. The method of claim 5,
In order to allow all user terminals to receive a signal with the same power in the downlink of the cell-free MIMO system, the transmit power allocated to the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. ,
Beamforming and power allocation method. 7. The method of claim 6,
SINR values of signals transmitted to user terminals in the downlink of the cell-off MIMO system do not have the same value,
Beamforming and power allocation method. The method according to claim 1,
The uplink reception beamforming vector is determined by a ZF (zero forcing) method,
Beamforming and power allocation method. 9. The method of claim 8,
The optimal beamforming vector of the ZF method is selected from a left generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals,
Beamforming and power allocation method. The method according to claim 1,
The downlink transmission beamforming vector is determined by the ZF method,
Beamforming and power allocation method. 11. The method of claim 10,
The optimal beamforming vector of the ZF method is selected from a right generalized inverse matrix, minimizing interference between user terminals, and maximizing a minimum SINR value of a signal received from the user terminals,
Beamforming and power allocation method. A cell-free MIMO system comprising a central processing unit (CPU) and a plurality of access points (APs), comprising:
a CPU for determining an uplink reception beamforming vector for each user terminal and a downlink transmission beamforming vector for each user terminal, and transmitting the determined uplink reception beamforming vector for each user terminal to the plurality of APs; and
A plurality of APs that perform uplink reception using the uplink reception beamforming vector and perform downlink transmission using the downlink transmission beamforming vector,
A cell-free MIMO system. 13. The method of claim 12,
The uplink reception beamforming vector is determined by a maximum ratio combining (MRC) method,
Cell-free MIMO system. 14. The method of claim 13,
In order to receive the received signals from all user terminals with the same power in the uplink of the cell-free MIMO system, the transmit power of the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. ,
A cell-free MIMO system. 13. The method of claim 12,
The downlink reception beamforming vector is determined by MRT (maxium ratio transmission) method,
Cell-free MIMO system. 16. The method of claim 15,
In order to allow all user terminals to receive a signal with the same power in the downlink of the cell-free MIMO system, the transmit power allocated to the first user terminal is allocated in inverse proportion to the channel size value of the first user terminal. ,
Cell-free MIMO system. 13. The method of claim 12,
The uplink reception beamforming vector is determined by a ZF (zero forcing) method,
Cell-free MIMO system. 18. The method of claim 17,
The optimal beamforming vector of the ZF method is selected from a left generalized inverse matrix, minimizes interference between user terminals, and maximizes a minimum SINR value of a signal received from the user terminals,
Cell-free MIMO system. 13. The method of claim 12,
The downlink transmission beamforming vector is determined by the ZF method,
Cell-free MIMO system. 20. The method of claim 19,
The optimal beamforming vector of the ZF method is selected from a right generalized inverse matrix, minimizing interference between user terminals, and maximizing a minimum SINR value of a signal received from the user terminals,
Cell-free MIMO system.

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