KR101080012B1 - User Selection Method using Efficient Channel Feedback in Multiple Antenna System and Base Station Using the same - Google Patents

Abstract

The present invention relates to a user selection method using efficient channel feedback in a multi-antenna system, wherein a plurality of user terminals exist in a cell and a user terminal selection method in a multi-antenna system using a plurality of subchannels. Receiving channel gain measurement results of at least one subchannel including a maximum channel gain value from each of the user terminals; (b) arranging the respective user terminals based on the received channel gain measurement results of the subchannels; (c) determining the number of subchannels to which each user terminal should feed back in a next period according to the order of each sorted user terminal; (d) transmitting, to each of the user terminals, the number of subchannels to which the determined user terminal should feed back in a next period; And (e) allocating resources based on channel quality information of the subchannels fed back from the respective user terminals as a result of the transmission.

User, terminal, selection, subchannel, allocation, channel, feedback, gain, multiple, antenna, MIMO

Description

User Selection Method Using Efficient Channel Feedback in Multiple Antenna System and Base Station Using the same}

The present invention relates to a user selection method in a multi-antenna system and a base station apparatus using the same. More particularly, the present invention relates to a subchannel transmitted from a user terminal in a multiple subchannel communication network using zero-forcing beamforming (ZFBF). A multi-antenna system for arranging user terminals according to the gain of the UE, determining the number of subchannels to be fed back in the next period, and notifying each user terminal, and as a result, selecting the user terminal using the subchannel information fed back from the user terminal. It relates to a user selection method and a base station apparatus using the same.

Multiple Input Multiple Output (MIMO) is a system capable of obtaining high data throughput in a wireless communication system, and many studies have been conducted. In particular, techniques for avoiding interference between users in order to maximize channel capacity in such a multi-antenna system have been of great interest.

The most effective method of avoiding interference between users is the 'Dirty Paper Coding' technology. Although the DPC technology can achieve the maximum channel capacity in a multi-user environment, the complex encoding and decoding ( There is an implementation difficulty due to the decoding overhead.

As an alternative to solve the problems of the DPC technology, there is a beamforming technique having a lower complexity than the DPC technology. Among the beamforming techniques, 'Zero-Forcing Beamforming' (ZFBF) technology, which designs a beamforming weight vector to avoid interference between users, has been spotlighted in a simple and efficient manner.

However, this ZFBF technology is limited by the number of transmit antennas and the number of receive antennas, so when a large number of users exist in a cell, a base station (BS) selects as many users as possible from a large number of users. Should be. At this time, when searching in a 'brute-force' manner, that is, a method of finding the best user combination by considering all possible user combinations ensures to find an optimal user combination. However, the 'brute-force' approach creates great complexity when the number of users in the system's cells increases.

In order to solve the above problems, low complexity user selection techniques have been proposed. Assuming complete channel information is known at the base station, the base station selects a user with a high channel gain but with a channel direction that is well aligned with the zero-forcing beam direction, thereby providing low complexity for a system using ZFBF. User selection is possible. Recently, a method of semiorthogonal user selection (SUS) has been proposed that can achieve a throughput close to that obtained when the DPC technique is applied.

Most user selection techniques including SUS method have been proposed considering single subchannel system. Unlike a single subchannel system, in a multiple subchannel system, when a multipath exists, the subchannel has a frequency selective characteristic having a different channel state for each subchannel. In this case, by using a subchannel having a good channel gain, the system throughput may be higher than that of a single subchannel.

However, when a user is selected in consideration of all subchannels of all users in order to obtain a multi-user diversity gain in a multi-sub channel system, a problem of causing a large complexity with a large amount of channel feedback as the number of users existing in the system increases. There is this. Therefore, even in the case of a large number of users, an effective channel feedback method for minimizing the increase in the amount of channel feedback and a user selection method accordingly are required.

According to the present invention, the number of subchannels to be fed back in the next period is determined by sorting the user terminals according to the gain of the subchannels transmitted from the user terminals in the multi-subchannel communication network, and notified to each user terminal. It is an object of the present invention to provide a user selection method and a base station apparatus using the same in a multi-antenna system for selecting a user terminal using the received subchannel information.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention which are not mentioned can be understood by the following description, and will be more clearly understood by the embodiments of the present invention. Also, it will be readily appreciated that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the claims.

The method of the present invention for achieving the above object is a method of selecting a user terminal in a multi-antenna system using a plurality of user terminals in a cell and using a plurality of subchannels, the method comprising: (a) each of the user terminals; Receiving a channel gain measurement result of at least one subchannel including a maximum channel gain value from the apparatus; (b) arranging the respective user terminals based on the received channel gain measurement results of the subchannels; (c) determining the number of subchannels to which each user terminal should feed back in a next period according to the order of each sorted user terminal; (d) transmitting, to each of the user terminals, the number of subchannels to which the determined user terminal should feed back in a next period; And (e) allocating resources based on channel quality information of the subchannels fed back from the respective user terminals as a result of the transmission.

In addition, the base station apparatus according to the present invention is a base station apparatus using multiple antennas and providing a service to a plurality of user terminals in a cell using multiple subchannels, the channel gain of the subchannel received from each of the user terminals Arranging the respective user terminals in descending order, determining the number of subchannels to which each user terminal should feed back in the next cycle according to the sorted order, and notifying each of the user terminals. A scheduler for allocating resources based on channel quality information of the subchannel fed back from the user terminal; A beamformer for beamforming a downlink packet allocated by the scheduler; And the multiple antennas for transmitting a beamformed signal by the beamformer.

The present invention as described above has the effect of reducing the burden on the channel feedback for the user terminals having a poor channel gain by using less channel information feedback from the user terminals having a poor channel gain. In addition, the present invention presents the amount of feedback required for each resource allocation scheme, thereby reducing the size of the matrix that is the initial value of the user selection technique algorithm even when the user terminal is greatly increased, resulting in low complexity user selection. Technology can be implemented.

BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, It can be easily carried out. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a temporary configuration diagram of a cooperative multiple input multiple output (MIMO) system for downlink resource allocation to which the present invention is applied, and precoding is applied to one subchannel. The structure of the city user selection technique is shown.

As shown in FIG. 1, a base station 10 generally includes a plurality of transmit antennas 11, a scheduler 12, a power allocator 13, and a zero-forcing beamformer 14. ). In addition, a plurality of user terminals 20 exist in a cell managed by one base station. That is, one base station 10 supports data transmission for K user terminals 20, and each user terminal 20 transmits channel quality information (CQI) to the base station 10. Feedback.

It is assumed that the base station 10 has M transmit antennas and each user terminal 20 has one antenna. When the downlink packet to be transmitted to each user terminal 20 is transmitted, the base station 10 selects M user terminals 20 which are the maximum number of users that can be supported simultaneously from among the user terminals 20. At this time, assume that K ≥ M. The base station goes through a process of transmitting a signal for the selected M user terminals. The base station selects user terminals for a total of N subchannels. It is assumed that each user terminal undergoes independent large scale fading and small scale fading (eg multipath effect). As a result, each subchannel has a different channel state.

는 다음의 수학식 1과 같이 나타낼 수 있다.Here, the received signal of the n-th subchannel to the k-th user terminal 20 in the base station 10

Can be expressed as Equation 1 below.

은 기지국(10)으로부터 전송된 심볼 벡터이며,

은 n번째 부채널에 대한 k번째 사용자 단말(20)의 채널 행렬로

와 같이 정의되며, 대규모 페이딩(

)과 소규모 페이딩(

)을 모두 고려한 채널 행렬이다.

은 크게 쉐도잉 효과(shadowing effect)와 경로손실 효과(pathloss effect)가 고려된 매크로스코픽 페이딩(macroscopic fading)을 나타낸다.

는 n번째 부채널에 대한 k번째 사용자 단말(20)이 겪는 부가백색가우시안잡음(AWGN: Additive White Gaussian Noise)이다.

의 각 성분과

는 0의 평균과 1의 분산을 가진다. 기지국(10)은 각 부채널에 대한 평균전력 제약인

과 총 평균전력 제약인

를 가진다. 이때, 부채널별 전송신호의 전력은

과 같이 제약을 받게 되며, 총 전송신호의 전력은

와 같이 제약을 받는다.In Equation 1

Is a symbol vector transmitted from the base station 10,

Is the channel matrix of the k th user terminal 20 for the n th subchannel.

Defined as

) And small fading (

) Are all channel matrices.

Shows macroscopic fading that considers the shadowing effect and the pathloss effect.

Is the additive white Gaussian noise (AWGN) experienced by the k-th user terminal 20 for the n-th subchannel.

With each component of

Has a mean of zero and a variance of one. The base station 10 is an average power constraint for each subchannel

And the total average power constraint

Has At this time, the power of the transmission signal for each subchannel

And the total power of the transmission signal is

Are constrained,

As a result of the experiment, the power allocator 13 allocates power with the total power constraint P or if each subchannel allocates power with the power constraint P ⁿ , the performance is similar in both cases. In the simulation, we consider the case where each subchannel has power allocation with power constraint P ⁿ . It is assumed that the base station 10 has channel state information completely.

Next, the beamforming (BF) technique, which is known to achieve similar performance to the DPC technique by allocating different beamforming directions between users, will be described.

과

그리고

을 각각 n번째 부채널의 사용자 단말 k에 대한 데이터 심볼과 빔포밍 가중치 벡터 그리고 전송전력 스케일링 변수라고 하면, n번째 부채널에 대한 전송신호는

으로 표현된다. 이 경우 수학식 1을 다음의 수학식 2와 같이 다시 나타낼 수 있다.

and

And

If the data symbol, the beamforming weight vector and the transmission power scaling variable for the user terminal k of the nth subchannel, respectively, the transmission signal for the nth subchannel

. In this case, Equation 1 may be represented again as in Equation 2 below.

In Equation 2, the first term is a desired signal, the second term is an interference signal, and the last term is noise. By treating the second and third terms as noise, the system capacity obtained when the beamforming is performed can be expressed as Equation 3 below.

In this case, it can be expressed by Equation 4 that the optimal beamforming technique shows the same increase rate as the DPC technique.

In the present invention, a description will focus on the zero-forcing beamforming (ZFBF) technique, which is a simple form among the beamforming techniques showing the performance close to that of the DPC technique.

를 만족한다.ZFBF technology is designed to avoid the interference between the user terminal 20, the beamforming vector

.

의 부분집합이다. 다음의 수학식 5와 같이 Wⁿ(Sⁿ)을 Hⁿ(Sⁿ)의 슈도인버스(pseudoinverse)로 설계함으로써, 사용자 단말(20) 간의 간섭을 주지 않도록 시스템을 설계할 수 있게 나타낼 수 있다.Referring to FIG. 1, a set of user terminals 20 determined to be supported for subchannel n is called S ⁿ , and S ⁿ is defined as a subset of {1, ..., k} as a whole set. . At this time, the size of S ⁿ must be equal to or smaller than M, which is the number of simultaneous supportable user terminals 20. At this time, the channel and the beamforming weights corresponding to a selected user terminal 20 is defined as ^{H n (S n), H} n (S n) is

Is a subset of. By designing W ⁿ (S ⁿ ) as a pseudoinverse of H ⁿ (S ⁿ ) as shown in Equation 5, the system can be represented so as not to interfere with the user terminal 20.

In this case, Equation 3 may be represented again as in Equation 6 below.

은 다음의 수학식 7과 같이 표현할 수 있다.At this time, the effective channel gain of the i-th user

May be expressed as in Equation 7 below.

은 다음의 수학식 8에서와 같이 워터-필링(water-filling) 방식을 사용하여 쉽게 계산할 수 있다.Optimum in Equation 6

Can be easily calculated using a water-filling method as shown in Equation 8 below.

In Equation 8, (x) ⁺ means max {x, 0}, and the water level μ is selected as a value that satisfies Equation 9 below.

As a result, when the ZFBF technique is used, a process of selecting a user set providing the maximum throughput among all possible S ⁿ may be represented as in Equation 10 below.

S ^n, which satisfies Equation 10 above, can be solved in a 'brute-force' manner using a computer power to solve a problem. That is, the data throughput for all possible S ⁿ is calculated and the S ⁿ having the highest data throughput is selected. However, this method has a problem that as the number of users K increases, it has a very large complexity.

To solve this problem, techniques with low complexity have been proposed. In the present invention, a description will be given of a semi-orthogonal user selection (SUS) technique. However, the method proposed in the present invention is expected to bring additional complexity reduction by applying to other techniques as well as the SUS method.

The SUS method is a technology for selecting users having good orthogonality of channels for each subchannel. Since ZFBF technology has multiple users in each subchannel and shares the corresponding subchannels, unlike the OFDMA system which selects one user with the best channel gain per subchannel, the ZFBF technology not only provides the channel gains of the users but also the selected users. Emphasis on orthogonality between channels. Accordingly, the user selection technique in the ZFBF scheme is selected not only by the user's channel gain but also by considering the orthogonality between the user's channel gain and the selected user's channels. The detailed algorithm of SUS method is as follows.

First, the variables used in the SUS method are initialized as in Equation 11 below.

의 성분들 중에서 기존 단계에서 선택된 사용자들

과 직교한 성분을 계산하여

라고 정의한다.Secondly, following the procedure of Equation 12,

Users selected in previous stages

Calculate the component orthogonal to

It is defined as.

로 정의된다.if i = 1

Is defined as

In the third step, the i-th user terminal is selected through the following equation (13).

인 경우, 다음의 수학식 14와 같이

에 대하여 준직교성을 만족하는 사용자들의 집합

을 계산하여 다음 반복 과정으로 넘겨주는 과정을 거친다.Finally, in the fourth step

In the case of,

Set of users that satisfy quasi-orthogonality for

Calculate and pass to the next iteration process.

의 크기를 줄여 복잡도를 줄이는 방법이 제안되었다.At this time, α has a value of 0 ≦ α ≦ 1. The appropriate value of α is determined by the number of users and shows the best performance when 0.2 ≦ α ≦ 0.6. By introducing α, each iteration will be used in the next iteration.

A method to reduce the complexity by reducing the size of is proposed.

The methods presented above have discussed techniques called sum rate maximization (SRM) or max sum rate, which are resource allocation methods to maximize system throughput.

Next, a brief description will be made of a resource allocation technique in consideration of fairness between users. In the simplest form of equity consideration, resource allocation techniques are round-robin. The round robin approach aims to provide equal opportunities for all users. In other words, until all users are supported, the previously supported users are not supported. Therefore, there is a problem that causes a large loss in terms of throughput of the system.

In order to solve this problem, a PF (Proportional Fairness) method has been proposed as a balance between the SRM method for maximizing system throughput and the round robin method for allocating resources in consideration of equity. The PF method may be expressed as in Equation 15 below.

는 선택된 사용자 단말(20) 집합과 같이 지원하였을 때 사용자 단말(20) k가 얻을 수 있는 데이터 전송률을 지칭한다.

는 사용자 단말(20) k가 선택되었을 때 구해지는 값이 아니며, 전체 사용자 단말(20) 선택이 이루어진 후에 정확한 값을 알 수 있다. 이는 한 부채널에 다수의 사용자 단말이 존재하는 ZFBF 기술을 비롯한 프리코딩(precoding)을 사용하는 시스템의 특징이라고 할 수 있다. 그리고 μ_k(t)는 다음의 수학식 16과 같이 매 시간 슬롯마다 갱신되는 과정을 거친다.In this case, μ _k (t) is defined as the inverse of the average data rate supported by the user terminal 20 as a priority variable of the user terminal 20,

Refers to a data rate that can be obtained by the user terminal 20 k when supported with the selected set of user terminals 20.

Is not a value obtained when the user terminal 20 k is selected, and the correct value may be known after the selection of the entire user terminal 20 is made. This may be a feature of a system using precoding, including ZFBF technology, in which a plurality of user terminals exist in one subchannel. Μ _k (t) is updated every time slot as shown in Equation 16 below.

In this case, t _c is a window size and selects an appropriate value. In conclusion, π ⁿ (i) in Equation 13 is modified and applied to Equation 17 in the case of the PF resource allocation technique.

는 n번째 부채널에서 사용자 단말(20) k가 선택되었을 때 근사적으로 얻을 수 있는 데이터 전송률로, 실제 값은 해당 부채널에 대해 모든 사용자 단말(20)이 선택된 후 수학식 6을 이용하여 구할 수 있으며, 이 값은 수학식 16에서 우선순위 변수를 갱신시킬 때 사용된다.

Is a data rate that can be obtained approximately when the user terminal 20 k is selected in the nth subchannel, and an actual value is obtained by using Equation 6 after all the user terminals 20 are selected for the corresponding subchannel. This value is used when updating the priority variable in (16).

The methods presented above are for a single subchannel system and require extension to multiple subchannel systems. In the case of extending to the multiple subchannel system, by selecting the user appropriately for each subchannel, it is possible to obtain improved system throughput in the multiple subchannel system compared to the single subchannel system.

2 is a graph illustrating an example of data throughput of a single subchannel and a multiple subchannel system. FIG. 2 shows data throughputs of a single subchannel and a multiple subchannel system according to the number of selected user terminals (M = 4, M = 2).

2 shows simulation results for the case of 10 multipaths. However, in this multi-subchannel system, it is difficult to know all the subchannel information at the base station (Full CSI), which requires a limited channel feedback scheme. In addition, when using the ZFBF technology in a multiple sub-channel system, the research of the user selection technology is insufficient. Accordingly, the present invention proposes a user selection technique that is practical and can reduce complexity for a multiple subchannel system.

As described above, the user selection technique in the ZFBF scheme selects the user terminal in consideration of the channel gain of the user and the orthogonality between the selected user channels. As a result, a user having a good channel gain with respect to the corresponding subchannel has a high probability of being selected.

The present invention utilizes this tendency to sort user terminals in descending order according to channel gain, so that users with good channel gain feed back a large number of subchannel information to the base station, and conversely, users with poor channel gain receive fewer By feeding the channel information back to the base station, a practical and additional complexity reduction can be obtained.

3 is a flowchart illustrating a user selection method using efficient channel feedback in a multi-antenna system according to the present invention.

In the initial state, each user terminal reports the subchannel having the maximum channel gain among all subchannels to the base station (301). Here, the user terminal may report channel gains for all subchannels, report channel gains of subchannels having a predetermined or more gain value, or report only one subchannel having the maximum channel gain among all subchannels. However, the base station needs to acquire only the subchannel having the maximum channel gain and the maximum channel gain information from each user terminal.

The base station sorts the user terminals in descending order based on the maximum channel gain of the subchannels transmitted from each user terminal (302). At this time, for the user terminals reporting the same channel gain, the sort order is determined using resource allocation fairness.

In operation 303, the base station determines the number of subchannels to which the respective user equipments arranged in descending order should be fed back in the next period. A method of determining the number of subchannels to which each user terminal should feed back in a next period may be expressed by Equation 18 below.

Here, M _{best , k} represents the number of subchannels to be fed back in the next period.

By appropriately adjusting m and β in Equation 18, it is possible to select an appropriate value for a system that aims to maximize system total throughput or resource allocation techniques such as PF and round robin.

That is, in Equation 18, m serves to adjust the y-intercept of M _{best, k} , and means the number of subchannels to which the first user terminal is to be fed back among the user terminals arranged in descending order. Β is a constant determined to correspond to the value of m, which plays a role of controlling the slope of an exponential function. Here, β corresponding to m may be determined through simulation. That is, m is 0 ≦ m ≦ N, and β may have a value between 0 and 1.

In the case of SRM technology, since resources are allocated in a direction that maximizes the total throughput of the system without considering equity between users, a user terminal having a good channel gain is likely to be repeatedly selected for several subchannels.

Considering this, SRM technology requires a large m value, and m is fixed to N to find the optimal m and β. On the other hand, in the case of the round robin technique, all users aim to have the same number of opportunities, and thus feed back the same number of subchannels for each user regardless of the channel gain of the user. In the case of PF technology, users who have already been allocated resources are somewhat less likely to be allocated resources, but the overall tendency is similar to that of SRM, and the channels of users whose channel status is poor compared to SRM will be considered important. Therefore, it is expected to have a large value of m as well as SRM, while having a small value of β in order to match the equity between users.

에 속하는 후보 사용자로 속하게 된다.As described above, in consideration of each allocation scheme, the base station receives some channels among the subchannels of the users, and the users receive only the subchannels reported by Equation 11 below.

It belongs to the candidate user belonging to.

In general, a multi-antenna base station includes a scheduler, a zero forcing beamformer, and a plurality of antennas. The user selection method of the present invention is performed by a scheduler in charge of resource allocation of a base station. The scheduler includes a channel quality information receiver for receiving channel quality information including channel gains for the subchannels from the user terminal for user selection, and a plurality of user terminals based on the channel gains of the subchannels received through the channel quality information receiver. The next order according to the number of sub-channels allocated to the user terminal sorting unit for sorting them in descending order and the first user terminal (the user terminal having the best channel gain of the sub-channel) among the user terminals sorted by the user terminal sorting unit. A subchannel number determination unit that determines the number of subchannels to which each user terminal should feed back in the next period based on a constant for determining the number of subchannel reductions of the user terminals of the user terminals, and each user determined by the subchannel number determination unit. A transmitter for notifying each user terminal of the number of subchannels to which the terminals should feed back; And a resource allocator for allocating resources based on channel quality information of the subchannels fed back from each user terminal.

의 크기를 줄임으로써, 곧 시스템의 복잡도를 개선해 줄 수 있을 것으로 기대할 수 있다. 큰 β값을 이용함으로써 도 4와 같이 사용자 수가 증가함에 따라 복잡도 감소를 기대할 수 있다. 자원할당 기술 중 PF나 라운드 로빈 기술의 경우에는 작은 m값을 이용함으로써 크게 복잡도를 감소시킬 수 있다.4 is a graph showing the number of subchannels used by each user terminal according to various resource allocation techniques (SRM, round robin, PF). As shown in Figure 4, the matrix that is the initial value used in equation (11)

By reducing the size of, we can expect to improve the complexity of the system soon. By using a large β value, as the number of users increases as shown in FIG. 4, the complexity may be expected to decrease. In the case of resource allocation techniques, PF or round robin technique can reduce complexity by using small m value.

A process of determining the number of subchannels to be fed back by each user terminal as described above will be described as an example.

The maximum channel gain of the subchannel reported by user terminal a is 10 dB, the maximum channel gain of the subchannel reported by user terminal b is 8 dB, the maximum channel gain of the subchannel reported by user terminal c is 9 dB, and the user terminal It is assumed that the maximum channel gain of the subchannel reported by d is 9 dB, and the maximum channel gain of the subchannel reported by user terminal e is 7 dB.

Then, when the user terminals a to e are arranged in descending order based on the channel gains, the user terminals a, c, d, b, and e are in order. Here, although the user terminal c and the user terminal d reported the same channel gain, the user terminal c is aligned before the user terminal d in terms of fairness because the user terminal d has been allocated more resources than the user terminal c. Based on these results, the base station first determines the number of subchannels to which the user terminal a should feed back in the next period. It is assumed that four subchannels are determined for the user terminal a among a total of 10 subchannels. Then, the number of subchannels to be fed back to the user terminal c in the next order is determined by Equation 18, and the number of subchannels to be fed back to other user terminals is determined in the same manner.

As described above, when the number of subchannels to be fed back for each user is determined, the base station notifies each user terminal of information on the determined subchannels to be fed back for each user (304). Then, each user terminal measures and reports the channel gains for the subchannels corresponding to the number of subchannels to be notified to the base station. When the base station receives the information on the subchannels from each user terminal (305), the base station performs resource allocation to each user terminal using the subchannel information fed back from each user terminal (306).

Hereinafter, a simulation result performed on the present invention will be described with reference to Tables 1, 5, 6, and 7. Table 1 below shows the parameters used in the simulation.

Parameter Description System bandwidth 10 MHz Number of subchannels (N) 16 Carrier frequency 3.5 GHz Number of users 30

)Total power constraint of base station (

) 0 dB Power Constraints for Each Subchannel (

) 0 dB / 16 Number of multipath (L) 10 The number of antennas in the base station (

) 2 Number of antennas of the user

) One User allocation scheme Equip-power allocation

0.6

Since the SRM or PF resource allocation method allocates a subchannel to a user having a good channel state, the channel state of the user selected for each subchannel is likely to be good. Therefore, the simulation results show that the power allocation results are very similar in the case of water-filling allocation or equal-power allocation among users. . Based on this tendency, the same power allocation scheme is used in this simulation.

의 값을 계산함으로써 얻어진다.In this simulation, using the values in [Table 2], throughput was calculated when AMC (Adaptive Modulation Coding) was applied to the downlink subchannel. Carrier to Noise Ratio (CNR) is calculated for each subchannel, and the MCS (Modulation & Coding Scheme) level is selected accordingly. The data rate obtained for each subchannel is shown in [Table 2]. have. CNR is for each subchannel

It is obtained by calculating the value of.

MCS
Level Modulations Coding
Rate CNR for 1%
PER (dB) Data Rate (kbps) One QPSK 1/12 -2.2 614.4 2 QPSK 1/6 0.1 1228.8 3 QPSK 1/3 2.9 2457.6 4 QPSK 1/2 6.0 3686.4 5 QPSK 2/3 10.2 4915.2 6 16QAM 1/2 10.9 7372.8 7 16QAM 2/3 15.2 9830.4 8 64QAM 2/3 20.2 14745.6 9 64QAM 5/6 28.6 18432

5 is a graph showing the performance of 'arithmetic mean throughput' when the present invention is applied to the SRM technique. That is, FIG. 5 shows the 'arithmetic mean throughput' performance when the algorithm proposed in the present invention is applied to a system using the SRM technique, which is an allocation technique aimed at maximizing 'arithmetic mean throughput'. The y-axis shows the normalization of 'arithmetic mean throughputs' when limited channel feedback is used as the 'arithmetic mean throughput' (maximum) of full CSI.

Performance comparison between the best-M (fixed m and β = 0), which is a method in which all users report a certain number of subchannels to the base station among the existing channel feedback algorithms, and the proposed algorithm (m = 16 and large β) For simulation, m and β having 90% of 'arithmetic mean throughput' of Full CSI were verified through simulation.

As shown in FIG. 5, it can be seen that 'normalized arithmetic mean throughput' has 90% when 'm = 6 and β = 0' and when 'm = 16 and β = 0.11'.

 In this case, 'm = 6, β = 0' requires a total of 180 subchannels, and 'm = 16, β = 0.11' requires a total of 162 subchannels. As a result, the present invention has the effect of reducing the total feedback amount required when applied to the system using the SRM technology by about 10% compared to Best-M.

6 shows that the effect of reducing the channel feedback burden for each user is shown.

The x-axis represents the user terminals after sorting the users in descending order according to the maximum channel gain, and the y-axis represents the actual channel feedback burden by dividing the number of subchannels that each user should feed back by the channel capacity of each user. The best channel feedback scheme, Best-M, is the result of the blue line in FIG. 6, and the red line represents the performance of the algorithm proposed in the present invention. As can be seen in FIG. 6, the present invention reduces the burden by about 1/6 compared to the existing Best-M algorithm in terms of the channel feedback burden of the user terminals (high index users) having poor channel conditions. It can be seen.

7 is a graph showing the performance of the 'geometric mean throughput' when the present invention is applied to the PF technology. In other words, FIG. 7 shows 'geometric mean throughput' performance when applied to a system using the PF technique, which is an allocation technique aimed at maximizing 'geometric mean throughput'.

7 shows normalization of the 'geometric mean throughputs' when the limited channel feedback is used as the 'geometric mean throughput' (maximum value) of the full CSI on the y-axis. Performance comparison between the best-M (fixed m and β = 0), which is a method in which all users report a certain number of subchannels to the base station among the existing channel feedback algorithms, and the proposed algorithm (m = 16 and large β) For simulation, m and β having 90% of the 'geometric mean throughput' of Full CSI were verified through simulation. As shown in FIG. 7, it can be seen that 'normalized geometric mean throughput' has 90% when 'm = 6 and β = 0' and when 'm = 16 and β = 0.08'.

In this case, 'm = 6, β = 0' requires a total of 180 subchannels, and 'm = 16, β = 0.08' requires a total of 202 subchannels. As a result, the present invention requires an increased amount of about 10% compared to Best-M on both sides of the total feedback required when applied to a system using the PF technique, but the advantages according to the present invention are as follows. You can check it through

8 shows an effect of reducing the channel feedback burden for each user. The x-axis represents users after sorting the users in descending order according to the highest channel gain, and the y-axis shows the actual channel feedback burden by dividing the number of subchannels that each user should feed back by the channel capacity of each user. The best channel feedback scheme, Best-M, is the result of the blue line in FIG. 8, and the red line represents the performance of the algorithm proposed in the present invention.

As shown in FIG. 8, it can be seen that the present invention reduces the burden by about one third compared to the existing Best-M algorithm in terms of channel feedback burden of users with poor channel conditions (high index users). .

In a real system, the channel feedback burden given to the user with the worst channel condition is considered more important than the total number of subchannels required for channel feedback. The algorithm proposed in the present invention can minimize the channel feedback burden to a user with poor channel conditions in a system using SRM and PF schemes compared to existing channel feedback algorithms.

On the other hand, the method of the present invention as described above can be written in a computer program. And the code and code segments constituting the program can be easily inferred by a computer programmer in the art. In addition, the written program is stored in a computer-readable recording medium (information storage medium), and read and executed by a computer to implement the method of the present invention. The recording medium may include any type of computer readable recording medium.

The present invention described above is capable of various substitutions, modifications, and changes without departing from the technical spirit of the present invention for those skilled in the art to which the present invention pertains. It is not limited by the drawings.

1 is a temporary configuration diagram of a multiple antenna (MIMO) system for downlink user selection to which the present invention is applied;

2 is a graph illustrating data throughput of a single subchannel and multiple subchannel systems;

3 is a flowchart illustrating a user selection method using efficient channel feedback in a multi-antenna system according to the present invention;

4 is a graph showing the total number of subchannel feedbacks according to various βs and the number of users;

5 is a graph showing the performance of 'arithmetic mean throughput' when the present invention is applied to an SRM technology;

6 is a graph showing a channel feedback burden for each user when the present invention is applied to an SRM technology;

7 is a graph showing the performance of the 'geometric mean throughput' when the present invention is applied to the PF technology,

8 is a graph showing a channel feedback burden for each user when the present invention is applied to a PF technology.

Claims (8)

A method for selecting a user terminal in a multi-antenna system in which a plurality of user terminals exist in a cell and uses a plurality of subchannels, (a) receiving channel gain measurement results of at least one subchannel including a maximum channel gain value from each of the user terminals; (b) arranging the respective user terminals based on the received channel gain measurement results of the subchannels; (c) determining the number of subchannels to which each user terminal should feed back in a next period according to the order of each sorted user terminal; (d) transmitting, to each of the user terminals, the number of subchannels to which the determined user terminal should feed back in a next period; And (e) allocating resources based on channel quality information of the sub-channel feedbacked from each user terminal as a result of the transmission; User terminal selection method in a multi-antenna system comprising a. The method of claim 1, In step (b), And sorting the respective user terminals in descending order based on the received channel gain measurement results of the subchannels. The method of claim 2, In step (c), The number of subchannels m to which the first user terminal is to be fed back and the number of subchannels to be fed back to the next user terminals among the user terminals arranged in the descending order are determined by using a constant β to determine the degree of decrease. A method for selecting a user terminal in a multiple antenna system, wherein each user terminal determines the number of subchannels to be fed back in a next period. The method of claim 3, wherein The constant (β) is set in correspondence with the number of sub-channel (m) to be fed back to the first user terminal according to the resource allocation method, user terminal selection method in a multi-antenna system. The method of claim 3, wherein In step (c), the number of subchannels (M _{best, k} ) to be fed back to a next period by a certain k-th user terminal is calculated as shown in Equation 19 below.

A base station apparatus using a multiple antenna and providing a service to a plurality of user terminals in a cell using multiple subchannels, The user terminals are arranged in descending order based on the channel gains of the subchannels received from the respective user terminals, and the number of subchannels to be fed back to the next cycle by each user terminal in the sorted order is determined. A scheduler for notifying a user terminal and allocating a resource based on channel quality information of a subchannel fed back from each user terminal as a result of the notification; A beamformer for beamforming a downlink packet allocated by the scheduler; And The multiple antennas for transmitting a beamformed signal by the beamformer Base station device comprising a. The method of claim 6, The scheduler, A channel quality information receiver configured to receive channel quality information including channel gains of subchannels from the respective user terminals; A user terminal alignment unit for sorting each user terminal in descending order based on the channel gain of the subchannel received through the channel quality information receiving unit; A subchannel number determining unit configured to determine the number of subchannels to which each user terminal should feed back in a next period according to the order of the sorted user terminals; A transmitting unit for notifying each of the user terminals of the number of subchannels to which the respective user terminal determined by the subchannel number determining unit should feed back in a next period; And Resource allocation unit for allocating resources based on the channel quality information of the sub-channel fed back from each user terminal Base station device comprising a. The method of claim 7, wherein The subchannel number determiner, The number of subchannels (m) to which the first user terminal is fed back and the number of subchannels to be fed back to the next user terminal among the user terminals arranged in the descending order are determined by using a constant (β). The base station apparatus for determining the number of sub-channels each user terminal should feed back in the next period.

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