KR101618467B1 - Base station and interference management method of base station - Google Patents

Abstract

Disclosed is a base station which is capable of allocating an optimum resource to a terminal located in a cell. An embodiment of the present invention: receives channel information from the terminal; calculates fitness of the location of a particle by using the channel information; updates the speed of the particle based on the fitness; updates the location based on the speed; determines whether the fitness reaches a target value; and allocates the resource to the terminal based on the determination.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a base station and a base station,

The following embodiments relate to a control technique of inter-cell interference occurring between a cell and a cell.

A terminal located in a cell may be subject to interference. For example, the terminal may receive interference from other terminals in the cell, and may be interfered by terminals located in other cells. The former is called intra-cell interference, and the latter is called inter-cell interference.

Interference can reduce the performance and throughput of the system including the terminal.

Korea Patent No. 1237355 (filed by Qualcomm, entitled " Intercell interference elimination ") filed on May 26, 2009 and registered on Feb. 20, 2013. The Korean patent discloses a method comprising: accessing a first identifier used by a serving base station to encode a first link; Accessing a second identifier used by an interfering base station to encode a second link; Receiving a signal comprising the first and second links; Estimating the second link using a corresponding identifier and removing the second link by removing the second link from the received signal; And decoding the first link from the received signal by channel estimation. ≪ RTI ID = 0.0 > [0011] < / RTI >

According to an embodiment of the present invention, inter-cell interference occurring between a cell and a cell can be minimized. In addition, according to an embodiment of the present invention, a base station can allocate an optimal resource to a terminal located in a cell through execution of a scheduling task.

A base station that performs an algorithm based on a particle cluster optimization technique according to one side includes a receiver for receiving channel information from a terminal; An operation unit for calculating fitness of a position of a particle using the channel information, updating a speed of the particle based on the fitness, and updating the position based on the speed; A determination unit for determining whether the fitness of the updated position has reached a target value; And an allocation unit allocating resources to the terminal using the updated location based on the determination.

An operation method of a base station performing an algorithm based on a particle cluster optimization technique according to one side includes: receiving channel information from a terminal; Calculating a fitness of the position of the particle using the channel information, updating the velocity of the particle based on the fitness, and updating the position based on the velocity; Determining whether the fitness of the updated location has reached a target value; And allocating resources to the terminal using the updated location based on the determination.

According to an embodiment of the present invention, inter-cell interference occurring between a cell and a cell can be minimized. In addition, according to an embodiment of the present invention, a base station can allocate an optimal resource to a terminal located in a cell through execution of a scheduling task.

1 is a diagram for explaining a system according to an embodiment.
FIG. 2 is a diagram for explaining a component carrier selection and scheduling algorithm (hereinafter referred to as an algorithm) of a base station according to an embodiment.
3 is a view for explaining a base station according to an embodiment.
Fig. 4 is a simulation result for explaining suitability according to the number of particles.
5 is a diagram illustrating a cumulative distribution function (CDF) of a min-max throughput of an algorithm according to an embodiment.
FIG. 6 is a view for explaining a result of a comparison between an algorithm according to an embodiment and an existing component carrier selection and scheduling algorithm.
7 is a diagram for explaining the fairness of the algorithm according to an embodiment and the fairness of the existing existing component carrier selection and scheduling algorithm.
FIG. 8 is a diagram for explaining a percentage gain of an algorithm according to an embodiment with respect to a conventional existing component carrier selection and scheduling algorithm.
9 is a view for explaining a method of operating a base station according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

Various modifications may be made to the embodiments described below. It is to be understood that the embodiments described below are not intended to limit the embodiments, but include all modifications, equivalents, and alternatives to them.

The terms used in the examples are used only to illustrate specific embodiments and are not intended to limit the embodiments. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises" or "having" and the like refer to the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

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

In the following description of the present invention with reference to the accompanying drawings, the same components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. In the following description of the embodiments, a detailed description of related arts will be omitted if it is determined that the gist of the embodiments may be unnecessarily blurred.

1 is a diagram for explaining a system according to an embodiment.

Referring to FIG. 1, a system according to one embodiment may include a plurality of cells 100-120. Each cell includes a base station 101 and a terminal 102.

Due to the plurality of cells, the cell 100 may receive interference from other cells 110 and 120 located around the cell, and the interference that the cell 100 receives from the other cells 110 and 120 may be referred to as intercell interference . Intercell interference can reduce the performance of the cell 100.

으로 표현되고, N개의 사용자는

으로 표현되며, L개의 컴포넌트 캐리어(즉, 컴포넌트 캐리어 세트)는

으로 표현될 수 있다.

번째 컴포넌트 캐리어의 리소스 블록(Resource Block, RB)은

개 있다. 이하, 일 실시예에 따른 시스템은 주파수 재사용 시스템(frequency reuse-1 system)으로 가정하고 설명한다. 하지만, 일 실시예에 따른 시스템은 다른 시스템까지 확장 가능하다.Suppose there are K base stations, there are N users, and there are N users in the cell. Assume also that there are L component carriers (CC), and there are M resource blocks. The K base stations

And the N users are represented by

, And L component carriers (i.e., a set of component carriers)

. ≪ / RTI >

The resource block (RB) of the ith component carrier

There are dogs. Hereinafter, a system according to an embodiment is assumed to be a frequency reuse-1 system. However, the system according to one embodiment is scalable to other systems.

으로 표현될 수 있다. 여기서, m번째 리소스 블록은

번째 컴포넌트 캐리어에 있는

개의 리소스 블록 중 어느 하나이다. 이하,

번째 컴포넌트 캐리어에 있는 m번째 리소스 블록을 m번째 RB라 한다. k번째 기지국이 m번째 리소스 블록을 통해 전송하지 않는다면

는 0일 수 있다. k번째 기지국의 전송 파워에 대한 제약이 존재할 수 있다. 즉, k번째 기지국의 전송 파워는 W이하일 수 있고,

으로 표현될 수 있다.The transmission power for the m-th resource block of the k-th base station is

. ≪ / RTI > Here, the m-th resource block

Th component carrier

Resource blocks. Below,

The m-th resource block in the ith component carrier is called the m-th RB. If the k-th base station does not transmit through the m-th resource block

May be zero. there may be a constraint on the transmission power of the k-th base station. That is, the transmission power of the k-th base station may be W or less,

. ≪ / RTI >

라 하자. 여기서, 채널은 패스 로스(path loss) 및 쉐도잉 효과(shadowing effect)를 고려한 채널일 수 있다. n번째 사용자가 k번째 기지국으로부터 수신한 파워는

일 수 있고, 나머지 사용자들에게

는 간섭일 수 있다.the channel gain of the channel formed between the k-th base station and the n-th user allocated to the m-th RB

Let's say. Here, the channel may be a channel considering a path loss and a shadowing effect. The power received by the n-th user from the k-th base station is

, And the remaining users

May be interference.

The SINR of the n-th user can be expressed by Equation (1).

[Equation 1]

는 m번째 RB에서 n번째 사용자가 받는 노이즈를 나타내고,

은 결정 변수(decision variable)를 나타낸다.

는 m번째 RB의 할당과 대응할 수 있다.

에 대해서 후술한다.In Equation (1)

Represents the noise received by the n-th user in the m-th RB,

Represents a decision variable.

May correspond to the allocation of the m < th > RB.

Will be described later.

(achieved throughput by nth UE)은 수학식 2로 표현될 수 있다.Throughput by the nth user in the assigned RB

(achieved through by nth UE) can be expressed by Equation (2).

&Quot; (2) "

는 채널의 대역폭을 나타낸다. N개의 사용자에 대한 채널이 동일한 대역폭을 가질 수 있고, 일 실시예에 따르면,

는 상수일 수 있다. n번째 사용자에 할당된 RB의 개수는 RB의 개수에 관한 수요(demand)

에 기초할 수 있다.

를 최대화하기 위해서 복수의 제약(constraint) 조건들이 고려될 수 있다.In Equation (2)

Represents the bandwidth of the channel. The channels for the N users may have the same bandwidth, and according to one embodiment,

Can be a constant. The number of RBs allocated to the n-th user is the demand for the number of RBs.

Lt; / RTI >

A plurality of constraint conditions may be considered to maximize.

&Quot; (3) "

&Quot; (4) "

&Quot; (5) "

&Quot; (6) "

&Quot; (7) "

&Quot; (8) "

&Quot; (9) "

&Quot; (10) "

를 최대화 하기 위한 수학식을 나타내고, 수학식 4 내지 수학식 10은 복수의 제약 조건들을 나타낸다. 수학식 4에서

은 n번째 사용자의 RB에 대한 수요를 나타내고,

은

번째 컴포넌트 캐리어에 있는

개의 리소스 블록 중에서 n번째 사용자에게 할당된 RB의 개수와 동일할 수 있다. 수학식 5는 동일한 셀에 위치한 서로 다른 사용자는 동일한 RB에 할당되지 않는 제약 조건을 나타낸다. 수학식 6은 최소 쓰루풋(

)에 대한 제약 조건을 나타내고, 수학식 7은 k번째 기지국이 전송할 수 있는 총 파워(total power budget)를 나타낸다. 수학식 10은 컴포넌트 캐리어의 총 개수는 3GPP LTE-Advanced 규격을 따름을 나타내는 제약 조건이다.Equation (3)

And Equations (4) to (10) represent a plurality of constraint conditions. In Equation 4,

Represents the demand for the RB of the n-th user,

silver

Th component carrier

The number of RBs allocated to the n-th user among the resource blocks may be equal to the number of RBs allocated to the n-th user. Equation (5) represents a constraint condition in which different users located in the same cell are not allocated to the same RB. Equation (6) represents the minimum throughput

And Equation (7) represents the total power budget that can be transmitted by the k-th base station. Equation (10) is a constraint condition indicating that the total number of component carriers conforms to the 3GPP LTE-Advanced standard.

Since inter-cell interference is considered, Equation (9) can be rewritten as Equation (11).

&Quot; (11) "

Component carrier selection and scheduling tasks of the base station may be possible within a plurality of the aforementioned constraints. More specifically, the base station can perform an autonomous distribution of RBs from the component carrier and minimize the effect of inter-cell interference.

Hereinafter, a component carrier selection and scheduling task of a base station according to an exemplary embodiment will be described.

FIG. 2 is a diagram for explaining a component carrier selection and scheduling algorithm (hereinafter referred to as an algorithm) of a base station according to an embodiment.

The algorithm according to one embodiment is based on Particle Swarm Optimization (PSO) algorithm. The location of all particles can be represented in M-dimensional space. Where M is the number of resource blocks in the component carrier. In the M-dimensional space, the position of the particle can be filled with positive integers ranging from 1 to N.

는

으로 표현될 수 있다.

은 m번째 RB의 할당을 나타낸다.Location of the jth particle

The

. ≪ / RTI >

Represents the allocation of the m-th RB.

Fitness may be calculated according to equation (12) in an iteration of the algorithm according to an embodiment. That is, the fitness of the current position of the jth particle can be calculated according to Equation (12).

&Quot; (12) "

the fitness of the current position of the jth particle can be calculated within the constraints of Equations (4) to (10).

의 적합성이 비교될 수 있다. 비교 결과, j번째 파티클의 현재 위치의 적합성이 더 높은 경우,

는 j번째 파티클의 현재 위치로 설정될 수 있다. 또한,

의 적합성과

의 적합성이 비교될 수 있고, 비교 결과,

의 적합성이 더 높은 경우,

는 j번째 파티클의 현재 위치로 설정될 수 있다. 좋은 적합성(good fitness)에 관하여 파티클의 위치 및 속도는 더 좋은 솔루션(better solution)의 성취를 가져올 수 있다.

및

에 대해선 후술한다.The suitability of the current position of the jth particle

Can be compared. As a result of the comparison, if the j-th particle is more suitable for the current position,

Can be set to the current position of the jth particle. Also,

Fitness of

Can be compared, and as a result of comparison,

If the suitability of < RTI ID = 0.0 >

Can be set to the current position of the jth particle. With respect to good fitness, the position and velocity of the particles can lead to the achievement of a better solution.

And

Will be described later.

The velocity and position of the particle can be updated according to (13) and (14).

&Quot; (13) "

&Quot; (14) "

The velocity of the particle can be updated via two positions. One is the personal best position (pbest) where the particle is visited, and the other is the finest position (nbest) where the particle and the neighbor of the particle are visited.

는 j번째 파티클의 새로운 속도,

는 j번째 파티클의 현재 속도,

는 j번째 파티클이 방문한 베스트 위치(best position),

는 j번째 파티클 및 j번째 파티클의 이웃(neighborhood) 파티클들이 방문한 최적 위치(finest position)를 나타낸다. 전체 군집(whole swarm)을 이웃이라 할 경우,

는 글로벌 베스트(global best, gbest)라 할 수 있고, 작은 이웃(small neighborhood)인 경우,

는 로컬 베스트(local best, lbest)라 할 수 있다. 글로벌 베스트와 로컬 베스트의 차이는 수렴(convergence)에 있다. gbest PSO는 lbest PSO보다 빠르게 수렴할 수 있다. 또한, lbest는 작은 샘플 공간(small sample space) 때문에 트랩될 가능성이 있다. 일 실시예에 따르면, gbest PSO가 사용될 수 있다.In Equation (13)

Is the new velocity of the jth particle,

Is the current velocity of the jth particle,

Is the best position visited by the jth particle,

Represents the finest position visited by the neighbor particles of the jth particle and the jth particle. When a whole swarm is called a neighbor,

Is a global best (gbest), and in the case of a small neighborhood,

Can be called local best. The difference between global best and local best is convergence. gbest PSO can converge faster than lbest PSO. Also, lbest is likely to be trapped because of the small sample space. According to one embodiment, gbest PSO may be used.

및

는 가속도 계수이고, P는 파티클의 총 개수이며,

및

는 0과 1사이의 랜덤 숫자이다.In Equation 12,

And

Is the acceleration coefficient, P is the total number of particles,

And

Is a random number between 0 and 1.

으로 표현될 수 있다.The velocity matrix of the particle can have N * M dimensions. Where N denotes the number of users located in the cell and M denotes the number of resource blocks in the component carrier. Every element of the velocity matrix has a real number between [N, N]. The boundary of the velocity matrix of the jth particle is

. ≪ / RTI >

The algorithm according to one embodiment is summarized as follows.

The base station can receive the channel information from the terminal. For example, the base station can receive the CQI from the terminal. An algorithm according to one embodiment may be performed based on the reception of channel information. Best solution information may be repeatedly obtained through the execution of the algorithm within the constraints of Equations (4) to (10). Through repetition, better particle velocity and particle position can be obtained. In the algorithm, if the relevance of the updated position of the particle to the new position of the particle, i. E. The suitability of the updated position of the particle, as a stopping condition, achieves the target value, the iteration may be interrupted. Alternatively, when the number of iterations is equal to or greater than a predetermined number, the stop condition can be achieved.

If an abort condition is achieved, the RB can be assigned to the updated position of the particle.

The base station may perform the algorithm depending on the local received information. The purpose of the algorithm is to maximize the capacity or throughput based on the demand of the user.

3 is a view for explaining a base station according to an embodiment.

3, the base station 300 includes a receiving unit 310, a calculating unit 320, a determining unit 330, and an assigning unit 340.

The receiving unit 310 may receive channel information from the terminal. The channel information may include, for example, a channel quality indicator (CQI). The CQI may be a value that reflects the reception SINR of the base station.

에 따라 단말의 SINR을 계산할 수 있다. 연산부(320)는 상기 SINR을 기초로 수학식

에 따라 쓰루풋

을 계산할 수 있다. 연산부(320)는

를 최대화하는 함수로 정의되는 적합성 함수

를 통해 입자의 위치의 적합성을 계산할 수 있다. 연산부(320)는 미리 정의된 제약 조건 이내에서 입자의 위치의 적합성을 계산할 수 있다. 보다 구체적으로, 연산부(320)는 수학식 4 내지 수학식 10의 제약 조건 이내에서 입자의 위치의 적합성을 계산할 수 있다. 예를 들어,

를 만족하는 범위에서 연산부(320)는 입자의 위치의 적합성을 계산할 수 있다.The calculating unit 320 may calculate the suitability of the position of the particle using the channel information. For example, the calculating unit 320 calculates

The SINR of the UE can be calculated. Based on the SINR, the computing unit 320 computes,

Throughput

Can be calculated. The calculating unit 320 calculates

Conformance function defined as a function that maximizes

The suitability of the position of the particles can be calculated. The calculation unit 320 can calculate the suitability of the position of the particle within the predefined constraint. More specifically, the computing unit 320 can calculate the fitness of the position of the particle within the constraints of Equations (4) to (10). E.g,

The computing unit 320 can compute the suitability of the position of the particle.

)의 적합성을 기초로 입자의 속도를 업데이트할 수 있다. 연산부(320)는 입자의 위치(

)의 적합성을 기초로 입자에 대한 제1 위치(

)를 계산할 수 있고, 상기 제1 위치의 적합성을 기초로 입자를 포함하는 그룹에 대한 제2 위치(

)를 계산할 수 있다. 여기서, 입자에 대한 제1 위치는 입자가 방문한 위치 중 가장 좋은 위치와 대응될 수 있고, 입자를 포함하는 그룹에 대한 제2 위치는 입자를 포함하는 군집이 방문한 가장 좋은 위치와 대응될 수 있다.In addition, the calculation unit 320 calculates the position

Lt; RTI ID = 0.0 > of particle < / RTI > The calculation unit 320 calculates the position

) ≪ / RTI > on the basis of the suitability of the particles

) And calculate a second position for the group containing particles based on suitability of the first position (

) Can be calculated. Here, the first position for the particle may correspond to the best position among the positions visited by the particle, and the second position for the group including the particle may correspond to the best position where the cluster including the particle visited.

)과 제1 위치의 적합성(

)을 비교할 수 있다. 연산부(320)는 입자의 위치의 적합성이 더 큰 경우 제1 위치를 입자의 위치로 결정하여 제1 위치를 계산할 수 있다. 또한, 연산부(320)는 제1 위치의 적합성(

)과 제2 위치의 적합성(

)을 비교할 수 있고, 제1 위치의 적합성이 더 큰 경우, 제1 위치를 입자의 위치로 결정할 수 있다. More specifically, the computing unit 320 computes the fitness of the position of the particle (

) And the fit of the first position (

) Can be compared. The calculation unit 320 can calculate the first position by determining the first position as the position of the particle when the fitness of the position of the particle is greater. Further, the calculating unit 320 calculates the fitness of the first position

) And the fitness of the second position (

), And if the fit of the first position is greater, the first position can be determined as the position of the particle.

및

이 계산된 후, 연산부(320)는 입자의 속도를 업데이트할 수 있다. 예를 들어, 연산부(320)는 수학식

에 따라 입자의 속도를 업데이트할 수 있다. 연산부(320)는 업데이트된 속도를 이용하여 입자의 위치를 업데이트할 수 있다. 예를 들어, 연산부(320)는 수학식

에 따라 입자의 위치를 업데이트할 수 있다.

And

The computation unit 320 can update the speed of the particles. For example, the calculating unit 320 calculates

The speed of the particles can be updated according to. The operation unit 320 can update the position of the particle using the updated velocity. For example, the calculating unit 320 calculates

To update the position of the particle.

에서 업데이트된 위치를 이용하여 리소스 블록을 단말에 할당할 수 있다.The determination unit 330 can determine whether the fitness of the position of the particle has reached the target value. For example, if the suitability of the updated position of the particle has reached the target value, the allocator 340 may allocate resources to the terminal. When the fitness of the updated position reaches the target value, the assigning unit 340 assigns the component carrier set

The resource block can be allocated to the terminal using the updated location.

When fitness does not reach the target value, the calculation unit 320 can compute the fitness of the updated position, and the operation of the calculation unit 320 described above can be repeated.

Since the matters described with reference to Figs. 1 and 2 can be applied to the matters described with reference to Fig. 3, detailed description will be omitted.

Fig. 4 is a simulation result for explaining suitability according to the number of particles.

4 shows a case where the number of particles is 4, a graph 2 shows a case where the number of particles is 8, a graph 3 shows a case where the number of particles is 12, And the number of particles is 20.

Referring to FIG. 4, when the number of particles is 12, the fitness result is the best.

5 is a diagram illustrating a cumulative distribution function (CDF) of a min-max throughput of an algorithm according to an embodiment.

In FIG. 5, a graph 1 represents a case of an initial population and a graph 2 represents a case where an algorithm according to an embodiment is applied. Referring to FIG. 5, the average minimum-maximum throughput can be improved when an algorithm according to an embodiment is applied. Also, for the initial population, the maximum throughput is 275 kbps, and when the algorithm according to one embodiment is applied, the maximum throughput is 350 kbps. When an algorithm according to one embodiment is applied, the minimum-maximum throughput can be improved.

FIG. 6 is a view for explaining a result of a comparison between an algorithm according to an embodiment and an existing component carrier selection and scheduling algorithm.

Existing component carrier selection and scheduling algorithms may include random, round robin, and proportional fair algorithms.

In FIG. 6, graph 1 represents an algorithm according to an embodiment, graph 2 represents a proportional fit algorithm, graph 3 represents a round robin algorithm, and graph 4 represents a random algorithm.

Referring to FIG. 6, as the number of users included in a cell increases, the average throughput per user decreases. However, when the number of users included in the cell is the same, the average throughput is the best when the algorithm according to one embodiment is applied. Also, as the number of users included in the cell increases, the gap between the graph (1) and the graph (2) increases. In the graphs (2) to (4), as the number of users increases, competition for the number of fixed RBs increases, and low performance may be exhibited as competition increases. If an algorithm according to one embodiment is applied, the competition may be low and the interference may be minimized.

7 is a diagram for explaining the fairness of the algorithm according to an embodiment and the fairness of the existing existing component carrier selection and scheduling algorithm.

에 따라 계산될 수 있다. 공정성이 클수록 더 좋은 성능을 나타낸다.Fairness can be used to evaluate the performance of the algorithm,

. ≪ / RTI > The higher the fairness, the better the performance.

Existing component carrier selection and scheduling algorithms may include random, round robin, and proportional fair algorithms. In FIG. 7, graph (1) represents an algorithm according to one embodiment, graph (2) represents a proportional fit algorithm, graph (3) represents a round robin algorithm, and graph (4) represents a random algorithm.

Referring to FIG. 7, the fairness of the algorithm according to an embodiment is larger than other algorithms. As the number of users increases, the fairness of all algorithms decreases. Also, as the number of users increases, the gap between the graph (1) and the graph (2) increases. Since the algorithm according to one embodiment provides optimized component carrier selection and resource allocation, the gap between graph (1) and graph (2) increases as the number of users increases.

FIG. 8 is a diagram for explaining a percentage gain of an algorithm according to an embodiment with respect to a conventional existing component carrier selection and scheduling algorithm.

In FIGS. 8A and 8B, the right upper-left lower diagonal line represents the percentage gain of the algorithm according to an embodiment with respect to the random algorithm, and the upper left-lower right diagonal line represents the round robin algorithm in one embodiment And the horizontal line represents the percentage gain of the algorithm according to an embodiment relative to the proportional process algorithm.

Referring to FIGS. 8A and 8B, as the number of users increases, gain in throughput and gain in fairness are increased. The gain may be increased due to an algorithm according to one embodiment that considers interference while allocating resources.

9 is a view for explaining a method of operating a base station according to an embodiment.

Referring to FIG. 9, a base station can receive channel information from a terminal (910). The channel information may include, for example, a channel quality indicator (CQI).

The base station may calculate the fitness of the position of the particle using the channel information, update the velocity of the particle based on fitness, and update the position based on the velocity (920).

에 따라 단말의 SINR을 계산할 수 있다. 또한, 기지국은 상기 SINR을 기초로 수학식

에 따라 쓰루풋

을 계산할 수 있다. 또한, 기지국은

를 최대화하는 함수로 정의되는 적합성 함수

를 통해 입자의 위치의 적합성을 계산할 수 있다. 기지국은 미리 정의된 제약 조건 이내에서 입자의 위치의 적합성을 계산할 수 있다. 보다 구체적으로, 기지국은 수학식 4 내지 수학식 10의 제약 조건 이내에서 입자의 위치의 적합성을 계산할 수 있다. 예를 들어,

를 만족하는 범위에서 기지국은 입자의 위치의 적합성을 계산할 수 있다.For example,

The SINR of the UE can be calculated. Further, the base station calculates, based on the SINR,

Throughput

Can be calculated. In addition,

Conformance function defined as a function that maximizes

The suitability of the position of the particles can be calculated. The base station can calculate the fitness of the position of the particle within predefined constraints. More specifically, the base station can calculate the fitness of the position of the particle within the constraints of Equations (4) to (10). E.g,

The base station can calculate the suitability of the position of the particle.

)의 적합성을 기초로 입자의 속도를 업데이트할 수 있다. 기지국은 입자의 위치(

)의 적합성을 기초로 입자에 대한 제1 위치(

)를 계산할 수 있고, 상기 제1 위치의 적합성을 기초로 입자를 포함하는 그룹에 대한 제2 위치(

)를 계산할 수 있다. 여기서, 입자에 대한 제1 위치는 입자가 방문한 위치 중 가장 좋은 위치와 대응될 수 있고, 입자를 포함하는 그룹에 대한 제2 위치는 입자를 포함하는 군집이 방문한 가장 좋은 위치와 대응될 수 있다.Further, the base station may determine the position

Lt; RTI ID = 0.0 > of particle < / RTI > The base station determines the position of the particle (

) ≪ / RTI > on the basis of the suitability of the particles

) And calculate a second position for the group containing particles based on suitability of the first position (

) Can be calculated. Here, the first position for the particle may correspond to the best position among the positions visited by the particle, and the second position for the group including the particle may correspond to the best position where the cluster including the particle visited.

)과 제1 위치의 적합성(

)을 비교할 수 있다. 기지국은 입자의 위치의 적합성이 더 큰 경우, 제1 위치를 입자의 위치로 결정하여 제1 위치를 계산할 수 있다. 또한, 기지국은은 제1 위치의 적합성(

)과 제2 위치의 적합성(

)을 비교할 수 있고, 제1 위치의 적합성이 더 큰 경우, 제1 위치를 입자의 위치로 결정할 수 있다. More specifically, the base station may determine the suitability of the position of the particle

) And the fit of the first position (

) Can be compared. The base station can calculate the first position by determining the first position as the position of the particle if the fitness of the position of the particle is greater. Further, the base station may determine the suitability of the first location

) And the fitness of the second position (

), And if the fit of the first position is greater, the first position can be determined as the position of the particle.

및

이 계산된 후, 기지국은 입자의 속도를 업데이트할 수 있다. 예를 들어, 기지국은 수학식

에 따라 입자의 속도를 업데이트할 수 있다. 또한, 기지국은 업데이트된 속도를 이용하여 입자의 위치를 업데이트할 수 있다. 예를 들어, 기지국은 수학식

에 따라 입자의 위치를 업데이트할 수 있다.

And

Is calculated, the base station can update the speed of the particle. For example,

The speed of the particles can be updated according to. In addition, the base station can update the position of the particle using the updated velocity. For example,

To update the position of the particle.

The base station may determine 930 whether the fitness of the position of the particle has reached the target value. For example, the base station may determine whether the fitness of the updated position of the particle has reached a target value. When the fitness has reached the target value, the allocator 340 may allocate resources to the terminal (940). If the fitness has not reached the target value, the base station may repeat step 920 and step 930.

1 through 8 can be applied to the matters described with reference to FIG. 9, detailed description will be omitted.

The algorithm according to one embodiment may be applied to intra band carrier aggregation and inter band carrier aggregation.

A base station according to an embodiment may perform an algorithm to distribute radio resources. The allocation of the optimal resource block can be realized according to the performance of the algorithm according to an embodiment, and inter-cell interference can be minimized. In addition, a base station according to an embodiment can perform a scheduling task without involvement of a centralized entity (e.g., Mobility Management Entity (MME)).

According to one embodiment, a single CQI may be used for a group of resource blocks for reduction of information exchange for reporting of resource blocks. In addition, at the base station, the average of the CQIs may be calculated for geographically adjacent terminals.

According to one embodiment, the base station may perform an algorithm for component carrier selection and scheduling tasks. More specifically, the base station may (1) generate a swarm that includes a plurality of particles. In addition, the base station may (2) initially allocate the velocity and position of the particle. The base station can calculate (3) the fitness of the position of the particle (i.e., the initially assigned position). The base station can (4) calculate the pbest and nbest of the particle. The base station can (5) update the particle's velocity and position. The base station can calculate (6) the suitability of the updated position of the particle. The base station can calculate (7) the pbest and nbest of the particle. The base station may determine (8) whether the fitness of the updated position has reached the target value. If the fitness of the updated position does not reach the target value, the base station can repeat the steps (5) to (8).

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

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

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

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

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

Claims (12)

A receiver for receiving channel information from a terminal;
An operation unit for calculating fitness of a position of a particle using the channel information, updating a speed of the particle based on the fitness, and updating the position based on the speed;
A determination unit for determining whether the fitness of the updated position reaches a target value; And
Based on the determination, allocating resources to the terminal using the updated location,
Lt; / RTI >
The operation unit,
Calculating a second position corresponding to a best position among the first position corresponding to the best position among the positions visited by the particle and the visited position of the cluster including the particle,
Calculating the first position based on a result of comparing the suitability of the position of the particle with the fit of the first position and comparing the suitability of the first position and the suitability of the second position, And determining the first position as the position of the particle when the conformity is greater than the fit of the second position.
Base station.
delete delete The method according to claim 1,
The operation unit,
Calculating the suitability of the position of the particle within a predefined constraint,
Base station.
5. The method of claim 4,
The predefined constraint condition may include:

Lt; / RTI >

Is the number of component carriers,

silver

The number of resource blocks in a component carrier,

Is a decision variable, which is either 0 or 1,

Represents the demand of the terminal for the resource block,
Base station.
6. The method of claim 5,
remind

silver

Component carrier

1 < / RTI > if a resource block is allocated to the terminal,

Component carrier

If a resource block is not allocated to the terminal,
Base station.
Receiving channel information from a terminal;
Calculating a fitness of the position of the particle using the channel information, updating the velocity of the particle based on the fitness, and updating the position based on the velocity;
Determining whether the fitness of the updated location has reached a target value; And
Allocating resources to the terminal using the updated location based on the determination;
Lt; / RTI >
Wherein updating the location comprises:
Calculating a second position corresponding to a best position among a first position corresponding to the best position among the positions visited by the particle and a position where the cluster including the particle visited,
Calculating the first position based on a result of comparing the suitability of the position of the particle with the fit of the first position and comparing the suitability of the first position and the suitability of the second position, And determining the first position as the position of the particle when the conformity is greater than the fit of the second position.
A method of operating a base station.
delete delete 8. The method of claim 7,
Wherein the updating comprises:
Computing the suitability of the position of the particle within the predefined constraints
/ RTI >
A method of operating a base station.
11. The method of claim 10,
The predefined constraint condition may include:

Lt; / RTI >

Is the number of component carriers,

silver

The number of resource blocks in a component carrier,

Is a decision variable, which is either 0 or 1,

Represents the demand of the terminal for the resource block,
A method of operating a base station.
12. The method of claim 11,
remind

silver

Component carrier

1 < / RTI > if a resource block is allocated to the terminal,

Component carrier

If a resource block is not allocated to the terminal,
A method of operating a base station.

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