KR101384869B1 - Receiver and method of restoring data using multiple antenna - Google Patents

Abstract

A method for restoring data using multiple antennas is provided. The data recovery method includes receiving a received signal through a plurality of antennas, and obtaining an interference canceling estimation signal from which the interference signal is removed by spatially demultiplexing the received signal using a reception beamforming matrix obtained by decomposing a channel matrix. do. Spatial demultiplexing can be used to minimize the influence of interference signals from neighboring base stations, and a beamforming gain can be obtained using the reception beamforming matrix. Therefore, it is possible to reduce intercell interference and improve reception performance.

MIMO, SD, ZF, MMSE, Beamforming

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a receiver and a data restoration method using multiple antennas,

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a wireless communication system, and more particularly, to a receiver and a data recovery method using multiple antennas.

The next generation multimedia wireless communication system, which is being actively researched recently, requires a system capable of processing various information such as image and wireless data and transmitting the initial voice-oriented service. The purpose of a wireless communication system is to allow multiple users to make reliable communications regardless of location and mobility. However, a wireless channel is a Doppler due to path loss, noise, fading due to multipath, intersymbol interference (ISI), or mobility of UE. There are non-ideal characteristics such as the Doppler effect. A variety of techniques are being developed to overcome the non-ideal characteristics of wireless channels and to increase the reliability of wireless communications.

MIMO (Multiple Input Multiple Output Antenna) technology improves data transmission / reception efficiency by using multiple transmit antennas and multiple receive antennas. MIMO technology can be broken down into multiple independent channels depending on the number of transmit antennas and the number of receive antennas. Each independent channel may be referred to as a spatial layer or a stream.

MIMO techniques include spatial diversity, spatial multiplexing, and beamforming. Spatial diversity is a technique that increases transmission reliability by transmitting the same data in multiple transmit antennas. Spatial multiplexing is a technique capable of transmitting high-speed data without increasing the bandwidth of the system by simultaneously transmitting different data from multiple transmit antennas. In general, when a spatial diversity technique is used, a data restoration method using a signal combining technique is used. Signal combining techniques include Selective Combining, Equal Gain Combining, and Maximum Ratio Combining. When a spatial multiplexing technique is used, each transmission signal may be separated using spatial demultiplexing (SD).

The beamforming is a technique for increasing a signal to interference plus noise ratio (SINR) of a signal by applying a weight according to changing channel information in multiple antennas. In a wireless communication system, a case where a base station sends a signal to a terminal through beamforming is called downlink beamforming in particular. Since the beamforming performance is proportional to the accuracy of the channel information fed back from the UE, the amount of feedback information must be increased in order to increase the accuracy of the channel information. The feedback information amount is related to the radio channel state of the uplink and the amount of radio resources allocated to the feedback.

Meanwhile, a wireless communication system provides a communication service by dividing a service area into a plurality of cells in order to overcome a limitation of a service area and a capacity of a user. This is called a multi-cell environment. Different frequency bands are used between adjacent cells and the same frequency band is used between cells far enough away so that the frequency band can be spatially reused. Since frequency bands can be reused spatially, it is possible to accommodate a sufficient number of users by increasing the number of channels in a plurality of cell distributions.

However, even if different frequency bands are used between adjacent cells, users located at the cell-to-cell boundary receive inter-cell interference due to interference signals from adjacent cells. Due to intercell interference, a user's transmitted or received signal may be degraded. In particular, when using a beamforming technique in a multi-cell environment, when the base stations of adjacent cells each transmit a signal by forming a beam pattern to terminals existing at similar locations, they may affect each other as interference signals. This interference signal has the most influence on the terminal located in the cell boundary region, which causes the reception performance degradation of the terminal.

Therefore, when receiving a beamformed signal, there is a need for a receiver and a data recovery method using multiple antennas that can reduce inter-cell interference and improve reception performance.

Disclosure of Invention Technical Problem [8] The present invention provides a receiver and a data restoration method using multiple antennas that can reduce inter-cell interference and improve reception performance.

In one aspect, a method for recovering data using multiple antennas is provided. The data recovery method includes receiving a received signal through a plurality of antennas, and obtaining an interference canceling estimation signal from which the interference signal is removed by spatially demultiplexing the received signal using a reception beamforming matrix obtained by decomposing a channel matrix. do.

In another aspect, a receiver using multiple antennas is provided. The receiver includes a beamforming spatial demultiplexer for obtaining an interference cancellation estimation signal from which an interference signal is removed by spatially demultiplexing the received signal using a received beamforming matrix obtained by decomposing a plurality of antennas and a channel matrix for receiving a received signal. do.

Spatial demultiplexing can be used to minimize the influence of interference signals from neighboring base stations, and a beamforming gain can be obtained using the reception beamforming matrix. Therefore, it is possible to reduce intercell interference and improve reception performance.

The following techniques can be used in various wireless communication systems. Wireless communication systems are widely deployed to provide various communication services such as voice and packet data.

1 shows a wireless communication system.

1, a wireless communication system includes a base station (BS) 1 and a mobile station (MS) 2. One base station 1 can provide services for at least one cell. A cell is a region where the base station 1 provides communication services. The base station 1 generally refers to a fixed station that communicates with the terminal 2 and may be referred to as a Node-B, a Base Transceiver System (BTS), an access point Can be called. The terminal 2 may be fixed or mobile and may be referred to by other terms such as a User Equipment (UE), a User Terminal (UT), a Subscriber Station (SS), a wireless device, Generally, a downlink means communication from the base station 1 to the terminal 2, and an uplink means communication from the terminal 2 to the base station 1.

There are no restrictions on multiple access schemes used in wireless communication systems. The wireless communication system can use various multiple access schemes such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), and Orthogonal Frequency Division Multiple Access (OFDMA).

Since the wireless communication system is a multi-cell environment, there are other cells adjacent to the cell to which the terminal belongs. The cell to which the UE belongs is called a serving cell, and another adjacent cell is called a neighbor cell. The neighboring cell is a region where other base stations provide communication services. A base station belonging to a serving cell is referred to as a Serving BS, and another base station belonging to a neighboring cell is referred to as a neighbor BS. The signal received by the terminal from the serving base station is a data signal, and the signal received from the adjacent base station is an interference signal.

When a spatial multiplexing technique is used, different data signals that are spatially multiplexed and transmitted may be separated into estimated signals for each data signal using spatial demultiplexing (SD). ZF (zero-forcing), minimum mean squared error (MMSE), etc. may be used as the SD technique. ZF is a method of separating an estimated signal for each data signal by multiplying a received signal by a pseudo-inverse matrix of a channel matrix. MMSE is a method of minimizing the mean square error (MSE) by reducing the detection error by considering the influence of noise. The SD technique can be used not only when one base station transmits different signals through a plurality of transmission antennas but also when a plurality of base stations transmit different signals. Accordingly, when the serving base station transmits the data signal and the neighbor base station transmits the interference signal, the terminal can separate the data signal of the serving base station by using the SD technique.

In a multi-cell environment, it is assumed that there are M _T base stations and the terminal uses M _R multiple antennas. A reception signal received by the UE through M _R multiple antennas may be referred to as r and may be represented by the following equation.

Here, H is an M _R xM _T channel matrix between M _T base stations and M _R multiple antennas, s is a transmission signal vector transmitted by M _T base stations, and n is a noise vector. The received signal vector is r = [r ₁ , r ₂ ,... , r _MR ] ^T , the transmit signal vector is s = [s ₁ , s ₂ ,... , s _MT ] ^T , the noise vector is n = [n ₁ , n ₂ ,. , n _MR ] ^T. An element of the channel matrix H may be h _{i , j} (1 ≦ i ≦ _{M R} , 1 ≦ j ≦ M _T , i, j is a natural number) and a complex Gaussian variable having a variance of 1.

The estimated signal obtained by using the SD technique is represented by y _SD , and can be expressed by the following equation.

Where W is an M _T x M _R weight matrix. The weighting matrix W is W = ( H ^H H ) ^-1 H ^H , and when using MMSE, W = ( H ^H H +? ² I ) ^{- 1} H ^H. (·) ^H is a Hermitian matrix, and (·) ^-1 is an inverse matrix.

2 is a block diagram illustrating a receiver using multiple antennas according to an embodiment of the present invention.

Referring to FIG. 2, the receiver 100 includes a demodulator 110, a channel estimator 120, a demapper 130, a decoder 140, and an outputter 150. The decoder 140 may include a beamforming decoder 141, a spatial demultiplexer 142, and an estimated signal selector 143. The receiver 100 includes M _R reception antennas 190-1, ..., 190-M _R (M _R ≥ 1, M _R is a natural number). The receiver 100 may be part of a terminal in downlink or part of a base station in uplink.

The received signal received through the receive antennas 190-1,..., 190 -M _R is demodulated by the demodulator 110. The received signal includes a data signal processed by a transmission beamforming vector transmitted by the serving base station and an interference signal transmitted by an adjacent base station. The channel estimator 120 estimates the channel, and the demapper 130 demaps the demodulated signal into the encoded data. The decoding unit 140 decodes the encoded data and restores the original data.

The beamforming decoder 141 processes the received signal by using the reception beamforming vector corresponding to the transmission beamforming vector. Through the received beamforming signal processing, an estimated signal for the data signal can be obtained. In this case, the reception beamforming vector may be obtained from the reception beamforming matrix obtained by decomposing the channel matrix.

The beamforming spatial demultiplex decoder 142 spatially demultiplexes a received signal by using a received beamforming matrix obtained by decomposing a channel matrix. Through spatial demultiplexing, an interference cancellation estimation signal for a data signal from which interference signals are removed can be obtained. The beamforming spatial demultiplexer 142 may use a spatial demultiplexing (SD) technique.

The estimation signal selector 143 selects one of the interference elimination estimation signal and the estimation signal as reconstructed data and sends it to the outputter 150. The criteria for selecting the estimated signal by the estimated signal selector 143 may be a Signal to Interference plus Noise Ratio (SINR), a Spectral Efficiency, a distance between the terminal and the serving base station.

The output unit 150 outputs the restored data as an output signal. The output signal is received as received data.

Hereinafter, a multi-cell environment including K base stations (K ≧ 1, K is a natural number) is considered. It is assumed that each base station includes M _T (M _T ≥ 1, M _T is natural numbers), and the terminal includes M _R (M _R ≥ 1, M _R is natural numbers) reception antennas. The received signal includes a data signal processed by a transmission beamforming vector transmitted by the serving base station and an interference signal transmitted by an adjacent base station.

3 is a flowchart illustrating a data restoration method using multiple antennas according to another embodiment of the present invention.

Referring to FIG. 3, a terminal receives a reception signal through a plurality of antennas (S110).

The received signal may be referred to as r and may be expressed as in the following equation.

Here, K is the number of base stations throughout the system, P _k is the received power for the downlink signal from the k-th base station, H _k is the channel matrix of M _R × M _T between the k-th base station and the terminal, x _k Is a transmission signal vector transmitted from the k-th base station and n is a noise vector. k = 1 is the index of the serving base station, k = 2,... Is the index of the neighbor base station. n may be an Additive White Gaussian Noise (AWGN) vector.

Any matrix may be decomposed using Singular Value Decomposition (SVD). The SVD may decompose an arbitrary matrix into a unitary matrix U , a diagonal matrix ∑, and V. The channel matrix H _k between the k-th base station and the terminal may be represented by the following equation using SVD.

Here, the Hermitian matrix U _k ^H in U _k is a receive beamforming matrix, V _k is the transmission beam-forming matrix, Σ is a diagonal matrix _k. U _k , the transmission beamforming matrix V _k , and the diagonal matrix ∑ _k can be expressed by the following equation.

Here, Σ is a diagonal matrix, the diagonal elements (λ ^k) are eigenvalues (singular value) of _k. The diagonal elements are assumed to be arranged in descending order such that λ ₁ ^k has the maximum value and λ _MR ^k has the minimum value.

The transmission signal vector x _k transmitted from the k-th base station may be represented by the following equation using a vector generated from the transmission beamforming matrix.

Here, v ₁ ^k is a column vector of the transmission beamforming matrix V _k of the k-th base station, which is a transmission beamforming vector related to the maximum eigenvalue λ ₁ ^k , and s _k is a transmission vector of the k-th base station. Signal information.

As such, the terminal receives a received signal including a data signal processed by a transmission beamforming vector transmitted by a serving base station through a plurality of antennas and an interference signal transmitted by an adjacent base station.

The terminal obtains an estimated signal for the data signal from the received signal using the received beamforming vector (S120). The receive beamforming vector corresponds to the transmit beamforming vector. The reception beamforming vector can be obtained from the reception beamforming matrix U _k ^H. An estimated signal detected from the received signal using the received beamforming vector may be referred to as y _EB and may be expressed by the following equation.

는 수신 빔포밍 벡터에 의해 변형된 잡음 성분이다.

는 다음 수학식과 같이 나타낼 수 있다.Here, w _EB is a received beamforming vector, w _EB = ( u ₁ ¹ ) ^H , the maximum eigenvalue λ ₁ ^k is the beamforming gain,

Is a noise component modified by the receive beamforming vector.

Can be expressed by the following equation.

As such, a method of obtaining an estimated signal from a received signal is referred to as an Eigen-beamforming (EB) technique.

In a multi-cell environment where neighboring cell interference is not considered, when each base station transmits a signal using beamforming to a terminal belonging to a service area, beamforming may obtain a beamforming gain by maximizing the SINR of the received signal. have. However, in an area where neighboring cell interference is large, such as a cell boundary, reception performance degradation of a terminal occurs due to an influence of an interference signal transmitted from a base station located in an adjacent cell. Therefore, when the base stations that have the greatest influence on the terminal at the cell boundary transmit the beamformed signal, there is a need for a reception signal processing scheme capable of minimizing the influence of neighboring cell interference without reducing the beamforming gain.

Hereinafter, a reception signal processing method capable of minimizing the influence of neighboring cell interference is referred to as an Eigen-beamforming with interference cancellation (EB-IC) technique. Now, the interference cancellation beamforming technique will be described.

The terminal obtains the interference cancellation estimation signal by using the reception beamforming matrix and the pseudo inverse matrix (S130). The pseudoinverse matrix can be obtained using the modified channel matrix using the receive beamforming matrix. The interference cancellation estimation signal for the data signal can be obtained by spatial demultiplexing the received signal using the reception beamforming matrix and the pseudo inverse matrix.

The received signal r can be expressed by the following equation using the number of receive antennas M _R.

Here, the index of the neighbor base station is k = 2, ... using the number of receiving antennas M _R. , M _R and k = M _R +1,... It can be divided by and K. k = 2,... The index group of, M _R can remove the interference signal through the interference cancellation beamforming technique, and k = M _R ,. The index group of, K cannot remove the interference signal through the interference cancellation beamforming technique.

In the beamforming technique, a signal is reconstructed in a terminal by using a reception beamforming vector ( u ₁ ¹ ) ^H generated through decomposition of a channel matrix using SVD. The interference cancellation beamforming technique recovers data using a new weight vector generated by combining the reception beamforming matrix U ₁ ^H and a pseudo-inverse matrix used in the SD technique.

By performing the signal processing on the reception signal r using the reception beamforming matrix U ₁ ^H , a modified reception signal vector can be obtained. The modified received signal vector is referred to as r 'and can be represented by the following equation.

Here, G is a modified channel matrix modified in consideration of a data signal transmitted from a serving base station and an interference signal transmitted from a neighboring base station, s is a transmission signal vector, and n 'is a noise signal vector. The transform channel matrix G , the transmission signal vector s , and the noise signal vector n ′ can be expressed by the following equation.

After signal processing using the reception beamforming matrix U ₁ ^H , a pseudo inverse matrix for using the SD scheme can be obtained through the modified channel matrix G.

When the ZF method is used as the SD method, the pseudo-inverse matrix can be expressed as the following equation.

Here, Z is a pseudo inverse, and z _i (1 ≦ _i ≦ _{M R} , i is a natural number) is the i th row vector of the pseudo inverse Z.

When the MMSE technique is used as the SD technique, the pseudo-inverse matrix can be expressed as the following equation.

Where Z is a pseudo inverse, σ ² is a variance of noise, and z _i (1 ≦ _i ≦ _{M R} , i is a natural number) is the i-th row vector of pseudo inverse Z.

Hereinafter, both pseudo-matrix when using the ZF technique or pseudo-matrix when using the MMSE technique are referred to as pseudo-matrix Z.

The signal can be reconstructed using the one row vector of the pseudoinverse matrix Z and the modified received signal r '. An interference cancellation estimation signal restored through the interference canceling beamforming technique y _{EB - IC} la, and can be expressed by the following mathematical expression.

는 변형된 잡음 성분이다. 변형된 잡음 성분

는

로 표현할 수 있다. 즉, 간섭제거 빔포밍 기법을 사용하여 λ₁ ¹의 빔포밍 이득과 동시에 간섭 제거로 인한 수신 성능 이득을 얻을 수 있다.Here, w _{EB -} and _IC is the weight vector, λ ₁ ¹ is the beamforming gain,

Is a modified noise component. Modified Noise Component

The

. That is, by using the interference cancellation beamforming technique, a beamforming gain of λ ₁ ¹ may be obtained and a reception performance gain due to interference cancellation may be obtained.

w _{EB - IC} is a weight vector for the interference cancellation beamforming technique, which can be expressed by the following equation.

The terminal selects one of the estimated signal and the interference cancellation estimation signal (S140). The criteria for selecting the estimated signal may be SINR, transmission efficiency, distance between the terminal and the serving base station.

4 is a flowchart illustrating a data restoration method using multiple antennas according to another embodiment of the present invention. 4 illustrates a case in which a terminal selects one of an estimated signal and an interference cancellation estimation signal as an effective SINR.

The terminal receives a received signal through a plurality of receive antennas (S210). In this case, the received signal includes a transmission beamformed data signal transmitted by the serving base station and an interference signal transmitted by an adjacent base station.

The terminal estimates channel information formed between the base stations and the terminal and estimates the strength of the noise signal (S220).

The UE calculates an effective SINR when using a beamforming technique and an effective SINR when using an interference cancellation beamforming technique (S230). Each effective SINR can be obtained using the channel information estimated at the terminal and the strength of the noise signal.

라 하고, 다음 수학식과 같이 나타낼 수 있다.When the estimated signal is detected using the beamforming technique, the effective SINR for the estimated signal is estimated.

It can be expressed as the following equation.

라 하고, 다음 수학식과 같이 나타낼 수 있다.When the interference cancellation estimation signal is detected using the interference cancellation beamforming technique, the effective SINR for the interference cancellation estimation signal is detected.

It can be expressed as the following equation.

와 간섭제거 빔포밍 기법의 유효 SINR인

를 비교하여 데이터 복원에 필요한 가중치 벡터를 결정할 수 있다(S240).The effective SINR of the beamforming technique,

SINR of the Interference Cancellation Interference Beamforming Techniques

The weight vector required for data reconstruction may be determined by comparing (S240).

This can be expressed as the following equation.

가 간섭제거 빔포밍 기법의 유효 SINR인

보다 크면, 검출 벡터 w는 수신 빔포밍 벡터 (u ₁ ¹)^H를 사용한다(S250).The effective SINR of the beamforming technique,

Is the effective SINR of the interference cancellation beamforming technique

If larger, the detection vector w uses the reception beamforming vector u ₁ ^{1 H} (S250).

가 빔포밍 기법의 유효 SINR인

보다 크면, 검출 벡터 w는 가중치 벡터 z ₁ U ₁ ^H를 사용한다(S260).The effective SINR of the interference cancellation beamforming technique

Is the effective SINR of the beamforming technique

If greater than, detection vector w uses weight vector z ₁ U ₁ ^H (S260).

The method of determining the detection vector w can be expressed as the following equation.

In this way, by calculating the respective effective SINRs for the estimated signal and the interference cancellation estimation signal, and comparing them, an estimated signal having a large effective SINR can be adaptively selected as an output signal (S270). The output signal can be represented by the following equation using the detection vector and the received signal.

Hereinafter, the simulation results of the reception performance of the receiver and the data restoration method will be described. The simulation environment is 19 multi-cell environments, and each base station has one base station. The distance from the center of the cell to the farthest distance of the cell is normalized to one. The terminal is a downlink situation in which a base station signal is received.

5 shows an example of a multi-cell environment for simulation. 5 shows only one serving cell (Cell ₀ ) among the 19 multiple cells and six neighboring base station cells (Cell ₁ to Cell ₆ ) surrounding the serving cell (Cell ₀ ). The serving BS cell Cell ₀ is a cell to which the serving BS 10 to which the MS 20 communicates and the neighbor BS cell cells Cell ₁ to Cell ₆ belong to the neighbor BSs 11 to 16. The serving base station 10 transmits a data signal to the terminal, and the signals of the neighboring base stations 11, ..., 16 act as an interference signal to the terminal. The terminal 10 includes N antennas.

Referring to FIG. 5, one base station is located at each center of each cell. The distance (D) from the center of each cell to the farthest distance of the cell is normalized to one. It is assumed that the distance between the serving base station 10 and each of the adjacent base stations 11, ..., 16 is equal to 2 (2D).

The distance between the serving base station 10 and the terminal 20 is d. The simulation is calculated on average when the terminal 20 is uniformly distributed on a concentric circle having the same radius as d.

FIG. 6 is a graph illustrating an average value of effective SINR according to a change in distance between a terminal and a serving base station. The graph x-axis is the normalized distance (d) between the terminal and the serving base station, and the y-axis is the average value of the effective SINR. The unit of the average value of the effective SINR is decibels (dB).

6 is a simulation result for a terminal using an interference cancellation beamforming technique (EB-IC) and a terminal using a beamforming technique (EB). The transmit antennas M _T and receive antennas M _R use two, three, four. In both schemes, as the distance d between the terminal and the serving base station increases, the average value of the effective SINR decreases. This is because the strength of the interference signal of the adjacent base station increases. Also, as the number of antennas used increases, the average value of the effective SINR increases.

When the distance d between the terminal and the serving base station is close, the effective SINR average value is higher when the beamforming technique is used than when the interference cancellation beamforming technique is used. When the distance d between the terminal and the serving base station arrives at a point, the curve using the beamforming technique and the curve using the interference cancellation beamforming technique intersect. When two transmitting antennas and two receiving antennas are used, the distance d between the terminal and the serving base station crosses around 0.7 to 0.8. As the number of antennas used increases, the distance d between the terminal where the two curves cross and the serving base station becomes smaller. After the intersection, the effective canceling beamforming technique has a higher effective SINR average value than the beamforming technique. Although the beamforming technique is advantageous for the reception performance when the UE is near the serving base station, the interference cancellation beamforming technique is capable of minimizing the interference signal transmitted from the neighboring base station in the cell boundary region where the interference signal is high. It shows an advantage for improvement. In addition, as the number of antennas used increases, the interval in which reception performance gain can be obtained increases when the interference cancellation beamforming technique is used. Therefore, it may be efficient to use adaptively at the point where the reception performance advantages of the beamforming technique and the interference cancellation beamforming technique change.

7 is a graph showing transmission efficiency according to the change in the number of transmitting antennas and receiving antennas. The graph x-axis is the number of transmit and receive antennas (M _T , M _R ), and the y-axis is the transmission efficiency (Spectral Efficiency). The unit of transmission efficiency is bps / Hz (bits per second / Hertz). 7 is a simulation result for a terminal using an interference cancellation beamforming technique and a terminal using a beamforming technique. The distance d between the terminal and the serving base station is 0.6, 0.8, 1.0.

Referring to FIG. 7, as the distance d between the terminal and the serving base station increases, the terminal using the interference cancellation beamforming technique is higher in transmission efficiency than the terminal using the beamforming technique. That is, as the terminal moves to the cell boundary, the interference cancellation beamforming technique may improve reception performance than the beamforming technique.

8 is a graph illustrating a cumulative distribution function (CDF) according to an effective SINR. The graph x-axis is the effective SINR and the y-axis is the cumulative distribution function. 8 is a simulation result for a terminal using only the beamforming technique and a terminal adaptively using the beamforming technique and the interference cancellation beamforming technique. The transmit antennas M _T and receive antennas M _R use two, three, four.

Referring to FIG. 8, in a constant CDF, the case where the beamforming technique and the interference cancellation beamforming technique are adaptively used has a higher effective SINR than when only the beamforming technique is used. That is, it indicates that the reception performance of the terminal using the adaptive reception method is further improved.

9 is a graph illustrating transmission efficiency according to a change in distance d between a terminal and a serving base station. The x-axis of the graph is a normalization distance (d) between the terminal and the serving base station, and the y-axis is the transmission efficiency. The unit of the average value of the transmission efficiency is bps / Hz. 9 is a simulation result for a terminal using a beamforming technique, a terminal using an interference cancellation beamforming technique, and a terminal adaptively using the beamforming technique and the interference cancellation beamforming technique. Two and four transmit antennas M _T and receive antennas M _R are used.

Referring to FIG. 9, when the interference cancellation beamforming technique is used at a cell boundary having a large distance d between the terminal and the serving base station, transmission efficiency is higher than that of the beamforming technique. In addition, the adaptive use of the beamforming technique and the interference cancellation beamforming technique has higher transmission efficiency than the case of using only the beamforming technique or only the interference cancellation beamforming technique. That is, the reception performance of the terminal using the instantaneous reception method is further improved.

The numerical values of the above-described simulation results are merely illustrative and not restrictive. The simulation results may vary depending on the given conditions. Even if the result of the simulation is changed, it does not depart from the technical idea of the present invention if it meets the intention of the present invention.

As described above, in case of a receiver using multiple antennas, SINR and transmission efficiency are greatly improved when adaptively using the beamforming technique and the interference cancellation beamforming technique instead of using only the beamforming technique or the interference cancellation beamforming technique. have. Therefore, the adaptive reception method can greatly improve the reception performance. This is because the adaptive reception method uses a beamforming technique in the region adjacent to the serving base station and an interference cancellation beamforming technique that can minimize the interference signal at the cell boundary.

All of the functions described above may be performed by a processor such as a microprocessor, a controller, a microcontroller, an application specific integrated circuit (ASIC), etc. according to software or program code or the like coded to perform the function. The design, development and implementation of the above code will be apparent to those skilled in the art based on the description of the present invention.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention. You will understand. Therefore, it is intended that the present invention covers all embodiments falling within the scope of the following claims, rather than being limited to the above-described embodiments.

1 shows a wireless communication system.

2 is a block diagram illustrating a receiver using multiple antennas according to an embodiment of the present invention.

3 is a flowchart illustrating a data restoration method using multiple antennas according to another embodiment of the present invention.

4 is a flowchart illustrating a method of restoring data using multiple antennas according to another embodiment of the present invention.

5 shows an example of a multi-cell environment for simulation.

FIG. 6 is a graph illustrating an average value of effective SINR according to a change in distance between a terminal and a serving base station.

7 is a graph showing transmission efficiency according to the change in the number of transmitting antennas and receiving antennas.

8 is a graph illustrating a cumulative distribution function (CDF) according to an effective SINR.

9 is a graph illustrating transmission efficiency according to a change in distance d between a terminal and a serving base station.

Claims (13)

Receiving a received signal through a plurality of antennas; And And obtaining an interference canceling estimation signal from which the interference signal is removed by spatially demultiplexing the received signal using the reception beamforming matrix obtained by decomposing a channel matrix. The method of claim 1, further comprising: obtaining a reception beamforming vector from the reception beamforming matrix, and obtaining an estimated signal from the received signal using the reception beamforming vector; And And selecting one of the interference cancellation estimation signal and the estimation signal. delete The method of claim 1, wherein the obtaining of the interference elimination estimation signal Obtaining a modified channel matrix using the receive beamforming matrix; Obtaining a pseudo-inverse matrix using the modified channel matrix; And Obtaining a weight vector using the reception beamforming matrix and the pseudo inverse matrix, and performing spatial demultiplexing using the weight vector to obtain the interference cancellation estimation signal. The method of claim 2, wherein the estimated signal having a large effective SINR is selected by comparing an effective signal-to-interference plus noise ratio (SINR) of the interference canceled estimation signal with an effective SINR of the estimated signal. . delete delete delete delete A plurality of antennas for receiving a received signal; And And a beamforming space demultiplex decoder for demultiplexing the received signal using a received beamforming matrix obtained by decomposing a channel matrix to obtain an interference cancellation estimation signal from which an interference signal is removed. 11. The apparatus of claim 10, further comprising: a beamforming decoder for obtaining a reception beamforming vector obtained from the reception beamforming matrix, and obtaining an estimated signal from the received signal using the reception beamforming vector; And And an output unit for selecting one of the interference elimination estimation signal and the estimation signal and outputting the output signal as an output signal. delete 12. The receiver of claim 11, wherein the output unit selects an estimated signal having a large effective SINR as the output signal by comparing the effective SINR of the interference cancellation estimation signal with the effective SINR of the estimated signal.

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