KR20180046350A - Mimo systems with independent oscillators and phase noise mitigation method thereof - Google Patents

Abstract

Disclosed is a method for removing phase noise of a multiple-input multiple-output system with an independent oscillator per antenna. The method comprises: a step of receiving a transmission signal with respect to data transmitted from a transmission antenna through a reception antenna and an oscillator; a step of predicting a plurality of parameters with respect to phase noise of a transmission end and phase noise of a reception end based on a mathematically modeled result of a signal transmitted and received through a multiple-input multiple-output system with an independent oscillator per antenna; and a step of removing the phase noise of the transmission end and the reception end, which is predicted from a reception signal. In this regard, the step of predicting a plurality of parameters includes: a step of repetitively calculating relational expressions with respect to the phase noise of the transmission end and the reception end in rotation; and a step of interrupting repetitive calculation and outputting data calculated in the previous repetition step if an error value obtained by repetitive calculation results is larger than an error value obtained in the previous repetition step.

Description

[0001] MIMO SYSTEMS WITH INDEPENDENT OSCILLATORS AND PHASE NOISE MITIGATION METHOD THEREOF [0002] BACKGROUND OF THE INVENTION [0003]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a wireless communication system, and more particularly, to a multiple-input multiple-output (MIMO) system for processing an algorithm for eliminating phase noise generated in a wireless communication system will be.

In recent years, in order to obtain a better channel environment in a MIMO system, the antenna spacing is designed to be reduced. In particular, as shown in FIG. 1, an oscillator has been commonly used in a multi-input / output system at an antenna stage. However, recently, as shown in FIG. 2, when a very high frequency is used, an oscillator is used independently at each antenna end due to a problem in implementation of an RF circuit.

Phase noise, on the other hand, appears as a random process in the form of noise due to the non-ideal characteristics of the oscillator. This phase noise appears independently for each oscillator. If an independent oscillator is used for each antenna stage, the number of phase noise affecting the system increases in proportion to the number of antennas. That is, there has been a problem that performance deterioration caused by phase noise becomes larger than in the conventional system using a common oscillator for the antenna end.

In particular, the phase noise caused by the non-ideal characteristics of the oscillator at the RF stage has a great influence on the performance of the Orthogonal Frequency Division Multiplexing (OFDM) system. Phase noise causes performance degradation in two forms: common phase error (CPE) and inter-carrier interference (ICI). Since the influence of the common phase error is larger among them, studies for estimating the common phase error have conventionally been actively conducted.

In this regard, Korean Patent Laid-Open No. 10-2003-0098224 (entitled "Phase Noise Reduction Device and Method of Wireless LAN System") discloses a method of correcting a common phase error correction algorithm and adjacent inter-subcarrier interference We propose a reduction algorithm. At this time, the common phase error correction algorithm is an extended method in the least squares method. However, the conventional phase noise cancellation method has a limitation in that only a single antenna is used.

As described above, in the case of a multi-antenna system having an independent oscillator for each antenna stage, there is phase noise in proportion to the number of antennas so that performance degradation is serious. However, in the past, It is true. In other words, the conventional least squares algorithm can predict only the common phase error among the effects caused by the phase noise. If only the common phase error is compensated, performance can be expected to be improved at low SNR power. It is difficult to do. In this case, it can be seen that an error floor occurs at a high SNR through a bit error rate (BER) performance graph.

Therefore, in a MIMO-OFDM system having an independent oscillator for each antenna stage, there is a need for a phase noise cancellation technique that can reduce the inter-subcarrier interference caused by phase noise as well as the common phase error.

An embodiment of the present invention is to provide a MIMO system and a phase noise reduction method capable of predicting and eliminating phase noise in a MIMO system having an independent generator for each antenna stage.

It should be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

According to an aspect of the present invention, there is provided a method for removing phase noise through a MIMO system having an independent oscillator for each antenna, the method comprising: receiving a transmission signal for data transmitted from a transmission antenna; ≪ / RTI > Predicting a plurality of parameters for a phase noise of a transmitter and a phase noise of a receiver based on a result of mathematically modeling a signal transmitted and received through a MIMO system having an independent oscillator for each antenna; And removing the phase noise of the transmitting end and the receiving end predicted from the received signal. At this time, the step of predicting the plurality of parameters includes: calculating the relational expression for the transmitting end and the receiving end phase noise alternately and repeatedly; And stopping the iterative operation and outputting the data calculated in the previous iterative step when the error value obtained according to the result of the repeated calculation is larger than the error value obtained in the previous iterative step.

According to a second aspect of the present invention, there is provided a MIMO system having an independent oscillator for each antenna, comprising: a plurality of reception antennas; A plurality of oscillators connected to each of the plurality of reception antennas; A memory for storing a program for removing phase noise of a receiving end and a transmitting end from a signal received via a receiving antenna and an oscillator; And a processor for executing the program. At this time, the processor estimates a plurality of parameters for the phase noise of the transmitter and the phase noise of the receiver based on a result of mathematically modeling a signal transmitted / received through the MIMO system as the program is executed, And eliminates the phase noise of the predicted transmitting and receiving ends. In this case, if the error value obtained according to the result of the iterative calculation is larger than the error value obtained in the previous iterative step, the iterative calculation is stopped And outputting the data calculated in the previous iteration step.

According to an embodiment of the present invention, since all the phase noise generated in a MIMO wireless communication system having an independent oscillator for each antenna stage is predicted, interference between adjacent subcarriers due to phase noise as well as common phase error Can also be reduced.

According to an embodiment of the present invention, the bit error rate (BER) performance can be greatly improved as compared with the phase noise cancellation method using only the existing least squares algorithm. If the number of pilot subcarriers is sufficient, System performance can also be improved.

1 is a diagram schematically illustrating the configuration of a conventional MIMO system using a common oscillator.
2 is a diagram schematically illustrating a configuration of a MIMO system using a conventional independent oscillator.
3 is a block diagram of a multiple input / output system having an independent oscillator according to an embodiment of the present invention.
4 is a flowchart illustrating a method of removing phase noise of a MIMO system according to an exemplary embodiment of the present invention.
Figure 5 is a flow diagram illustrating a detailed method by which the process of Figure 3 performs a regression algorithm in accordance with one embodiment of the present invention.
Figure 6 shows an overview of a regression algorithm in accordance with an embodiment of the present invention.
7 is a graph illustrating common phase error correction performance in a 2x2 MIMO system according to an embodiment of the present invention.
8 is a graph illustrating common phase error correction performance in a 5x5 MIMO system according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as " comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

Herein, the term " part " or " module " means a unit realized by hardware or software, a unit realized by using both, and a unit realized by using two or more hardware Or two or more units may be realized by one hardware.

Hereinafter, a multi-input / output system having an independent generator for each antenna stage according to an embodiment of the present invention and a method for removing phase noise through the multi-input / output system will be described in detail with reference to the drawings. For reference, in the embodiment of the present invention, it is assumed that the channel estimation is known because the channel estimation related technology is not related to the core technology of the present invention.

3 is a block diagram of a MIMO system according to an embodiment of the present invention.

As shown in FIG. 3, it is assumed that the MIMO system 100 according to an embodiment of the present invention is a wireless communication system using multiple antennas 110. The MIMO system 100 includes an MIMO-OFDM modulator 131, a MIMO-OFDM demodulator, and a demodulator, which are independent oscillator structures including an oscillator 120 for each transmitting / A memory 132, a memory 133, and a processor 134.

OFDM modulator 131 / demodulator 132 under the control of the processor 134. The transmitter / receiver antenna 110 receives the radio signal from the outside and outputs it to the MIMO-OFDM Modulator 131 and a demodulator 132. The oscillator 120 connected to each antenna performs a function of up-converting and down-converting a sub-carrier frequency.

The MIMO-OFDM modulator 131 performs a modulation process of modulating the data to be transmitted through each antenna 110 under the control of the processor 134. At this time, the modulation process can be performed in various ways. For example, the MIMO-OFDM modulator 131 modulates signals using a polarized multiplexing MIMO (polarity multiplexing MIMO) scheme in which a polarity is applied to a transmission signal according to the sign of a channel correlation transmitted through each antenna But is not limited thereto.

The MIMO-OFDM demodulator 132 under the control of the processor 134 performs a demodulation process of converting the signals input through the reception antenna 110 and the oscillator 120 into signals in the frequency domain. At this time, the demodulation process may be a reverse process of the modulation process.

The memory 133 stores one or more programs (or instructions) for the processor 134 to control the modulation and demodulation processes. In addition, the memory 133 stores a program for removing phase noise of a receiving end and a transmitting end from a signal received through the receiving antenna 110 and the oscillator 120. [ Meanwhile, the memory 133 may be referred to as a non-volatile storage device that retains stored information even when power is not supplied, and a volatile storage device that requires power to maintain stored information.

The processor 134 may be implemented with one or more components for controlling the MIMO system 100. The processor 134 may also execute a program stored in the memory 133 to perform a modulation process and a demodulation process for modulating or demodulating signals transmitted and received through the MIMO system 100. [ In particular, by executing the program stored in the memory 133, the processor 134 can improve the phase error correction performance of the MIMO system 100 by removing the phase noise generated in the oscillator of the transmitter and receiver stages following the demodulation process have. Hereinafter, this will be described in detail with reference to FIG.

4 is a flowchart illustrating a method for processor 134 to remove phase noise of MIMO system 100 in accordance with one embodiment of the present invention.

First, the processor 134 receives a transmission signal for data transmitted from the transmission antenna through a reception antenna and an oscillator (S410).

Thereafter, the processor 134 predicts a plurality of parameters for the phase noise of the transmitting end and the phase noise of the receiving end based on a result of mathematically modeling the signals transmitted and received through the multiple input / output system 100 (S420).

Hereinafter, a process of mathematically modeling signals transmitted and received through the MIMO system 100 will be described.

를 IDFT한 후, 시간 도메인에서 보내는 OFDM 신호(즉, 송신 신호)를 아래 수학식 1과 같이 표현할 수 있다.First, the data to be transmitted from the i < th >

The OFDM signal (i.e., a transmission signal) transmitted in the time domain can be expressed as Equation 1 below.

In Equation (1), the transmission signal is affected by the phase noise of the channel and the transmission terminal, and the reception signal received by the j-th reception antenna can be expressed by Equation (2) below.

및

는 각각 i번째 송신단과 j번째 수신단에서의 위상잡음을 의미하고,

는 j번째 수신단에서 가우시안 잡음을 의미하며,

는 순환 컨볼루션을 의미한다.

은 i번째 송신단 안테나와 j번째 수신단 안테나 사이의 채널을 의미한다.

및

은 각각 송신단과 수신단의 안테나 숫자를 의미한다.In Equation 2,

And

Denotes the phase noise at the i < th > transmitting end and the j < th >

Denotes the Gaussian noise at the jth receiving end,

Quot; refers to cyclic convolution.

Denotes a channel between the i-th transmitting-end antenna and the j-th receiving-end antenna.

And

Denotes the number of antennas of the transmitter and receiver, respectively.

If the received signal in Equation (2) is expressed as a vector, Equation (2) can be expressed as Equation (3) below.

The letters represented in Equation (3) represent a vector form of each character, a lower case represents a vector, and an upper case represents a matrix. Further, each character of Equation (3) is defined as the following Equation (4).

는 정규화 DFT(normalized discrete Fourier transform) 행렬을 나타내고,

는 벡터를 대각행렬(diagonal matrix)로 바꾸는 연산자로서, 같은 크기를 갖는 행렬들을 블록(block) 대각행렬로 바꾸는 연산자를 나타낸다. 또한, h는 채널 ,

는 송신단의 위상잡음,

는 수신단의 위상잡음을 나타낸다. In Equation 3,

Represents a normalized discrete Fourier transform (DFT) matrix,

Is an operator that converts a vector into a diagonal matrix and represents an operator that converts matrices having the same size into a block diagonal matrix. H is the channel,

The phase noise of the transmitting end,

Represents the phase noise of the receiving end.

의 분포를 따를 경우, 각 발진기에서 발생하는 위상잡음은 모두 독립적이므로, 수신단에서 발생되는 위상잡음은 다음의 수학식 5와 같이 나타낼 수 있다.It is assumed that the probability distribution of the phase noise is known in an embodiment of the present invention because the probabilistic characteristic can be known through the specifications of most oscillators. On the other hand, since most of the phase noise follows the Gaussian distribution, the phase noise generated at the j-

The phase noise generated in the receiver can be expressed by the following Equation (5): " (5) "

It can be said that the phase noise generated at the transmitting end follows the following equation (6).

Based on the system model described above, the processor 134 predicts the parameters of the phase noise of the transmitter / receiver. At this time, the processor 134 uses the MAP prediction technique to efficiently predict a large number of parameters from the limited signal.

(송신단의 위상잡음) 및

(수신단의 위상잡음)에 관한 로그가능도(log likelyhood: LLF) 함수는 아래 수학식 7과 같이 나타낼 수 있다.Specifically, the parameter to be predicted

(Phase noise of the transmitting end) and

A log likelihood (LLF) function related to the phase noise (phase noise of the receiving end) can be expressed by Equation (7) below.

및

(즉,

)를 산출한다. 이는, LLF 함수를 예측하고자하는 파라미터인

및

각각에 대해서 편미분을 수행하여, 편미분된 함수를 0으로 만드는

및

를 추출하는 것일 수 있다. The processor 134 applies the MAP (Maximum A Posterior) prediction technique to maximize the LLF function of Equation (7)

And

(In other words,

). This is because the LLF function is a parameter to be predicted

And

Perform a partial differentiation for each, and make the partial differentiated function 0

And

. ≪ / RTI >

Prior to this, Equation (3) can be expressed by simplifying Equation (8).

는

를 나타내며,

는

를 나타낸다. In the above equation,

The

Lt; / RTI >

The

.

The above Equation (7) can be expressed as Equation (9) by Equations (5), (6) and (8).

로 나타낼 수 있다. 이에 따라, 상기 수학식 9는 아래 수학식 10에서와 같이 다시 표현할 수 있다.Generally, since the phase noise is very small, it is possible to reduce the phase noise by Taylor's series

. Accordingly, Equation (9) can be expressed as Equation (10) below.

에 관하여 편미분 하면, 아래 수학식 11에 따라

을 구할 수 있다.The processor 134 determines the above equation (10)

, The following equation (11) is obtained.

Can be obtained.

에 관하여 편미분하면, 아래 수학식 12와 같이

을 구할 수 있다.Similarly, processor 134 may use Equation 10

, The following equation (12) is obtained.

Can be obtained.

,

및

의 값이 필요하다. On the other hand, in order to obtain the solutions of the equations (11) and (12)

,

And

Is required.

Therefore, the processor 134 repeatedly calculates the relational expression relating to the phase noise of the transmitting end and the receiving end, and stops repeating when the error value obtained according to the result of the repeated calculation becomes larger than that of the previous iteration, And outputs the obtained data. This will be described in detail with reference to FIG.

Referring to FIG. 5, the processor 134 calculates initial values of phase noise of a transmitter and a receiver (S421).

)를 산출한다. 그리고, 프로세서(134)는

및

를 수학식 11에 대입하여 송신단 위상잡음의 초기값(

)을 산출한다. 이어서, 프로세서(134)는

및

를 수학식 12에 대입함으로써 수신단 위상잡음의 초기값(

)을 산출한다.Specifically, the processor 134 calculates a common phase error using a Least Square algorithm and generates an initial transmission signal

). Then, the processor 134

And

(11) to obtain the initial value of the transmitting-end phase noise

). Subsequently, the processor 134

And

(12) to obtain the initial value of the phase noise of the receiving end

).

Then, based on the calculated initial values, the processor 134 repeatedly calculates a relational expression concerning the phase noise of the transmitting end and the receiving end.

)는, 다음의 수학식 13에서와 같이 수신 신호(y)에 k단계의 수신단 위상잡음(

)를 곱하여 수신단의 위상오차를 보정한다(S422).Specifically, the processor 134 receives the transmission data of the (k + 1)

(K) of the receiving-side phase noise (k) of the receiving signal y as shown in the following equation (13)

) To correct the phase error of the receiver (S422).

)에 대해 DFT를 수행한 후, 주파수 도메인에서 채널을 균등화(Equalization)함으로써 채널의 영향을 제거한다(S423). Next, the processor 134 compares the corrected received signal (

(S423), the channel effect is removed by equalizing the channel in the frequency domain.

라고 정의할 때, 이는 주파수 도메인에서의 신호이므로 송신단의 위상잡음에 영향받는다. 따라서, 프로세서(134)는 주파수 도메인에서의 위상잡음 벡터(

)를 이용하여 순환 행렬(circulant matrix)을 생성한 후, 생성된 순환 행렬을

에 곱해줌으로써, 송신단의 위상잡음의 영향이 제거된 데이터를 복원한다(S424). 상기한 과정은 다음의 수학식 14 및 15로 나타날 수 있다. On the other hand, after performing channel equalization,

, Which is a signal in the frequency domain, is influenced by the phase noise of the transmitting end. Thus, the processor 134 may determine the phase noise vector (< RTI ID = 0.0 >

) Is used to generate a circulant matrix, and the generated circulation matrix

Thereby restoring data from which the influence of the phase noise of the transmitter is removed (S424). The above process can be expressed by the following equations (14) and (15).

)와 수신단 위상잡음(

)을 수학식 11에 대입함으로써 송신단 위상잡음(

)을 산출하며, k+1 단계에서 획득된 송신 신호(

)와 산출된 송신단 위상잡음(

)을 수학식 12에 대입함으로써 수신단 위상잡음(

)을 산출할 수 있다(S425). 이때, 산출된 송신 신호(

), 송신단 및 수신단의 위상잡음(

,

)은 메모리(133)에 저장될 수 있다. The processor 134, through the above-described procedure, transmits the transmission signal (k + 1)

) And receiver phase noise (

) Is substituted into Equation (11)

), And the transmission signal obtained in the (k + 1)

) And the calculated transmit terminal phase noise (

) Into the equation (12) to obtain the receiver phase noise

) (S425). At this time, the calculated transmission signal (

), Phase noise of the transmitter and receiver (

,

May be stored in the memory 133. [

)은, 파일럿 서브캐리어를 이용하여 획득한 송신 신호(

)와의 차이를 통해서 산출될 수 있다. 이는 다음의 수학식 16과 같이 나타날 수 있다. Meanwhile, the processor 134 calculates an error value as an index of the phase error correction performance for each iteration step, and compares the error value with the error value in the previous iteration step (S426). If the error value is smaller than the error value in the previous iteration step, the processor 134 repeats the above-described process (moves to S422). If the error value is larger than the error value in the previous iteration step, (That is, the phase noise of the transmission signal and the transmission and reception ends) (S426). For example, the (k + 1) -th error value

) Is a transmission signal obtained by using the pilot subcarrier (

). ≪ / RTI > This can be expressed by the following equation (16).

는 파일럿 서브캐리어 인덱스의 집합을 나타낸다. 이상에서 설명한 회귀 연산 알고리즘의 개요는 도6과 같다.In the above equation,

Denotes a set of pilot subcarrier indices. The outline of the regression calculation algorithm described above is shown in Fig.

On the other hand, since the above algorithm is repeatedly calculated using the initial value, decreasing the number of iterations by accurately setting the initial value affects the reduction of the complexity of the algorithm.

)를 산출한 후, 송신단 위상잡음의 초기값(

)을 예측하기 위해서

이라고 가정을 하였다. 그러나, 실제 환경에서, 초기 송신 신호(

)는 수신단 위상잡음에 의한 인접 캐리어간 간섭을 포함한다. 즉, 수신단 위상잡음이 0일 가능성은 매우 낮다. Thus, the processor 134 may further perform an operation to set a more accurate initial value. That is, in the algorithm described above in FIGS. 4 and 5, the processor 134 corrects the common phase error,

), And then calculates an initial value (

To predict

. However, in an actual environment, the initial transmission signal (

) Includes adjacent intercarrier interference due to the receiving end phase noise. That is, the possibility that the phase noise of the receiving end is 0 is very low.

At this time, the influence of the phase noise can be expressed by the following Equation (17).

및

는 각각 i번째 송신단과 j번째 수신단의 위상잡음에 의해서 발생한 공통위상오차를 나타낸다. 또한,

및

는 원래의 행렬(

및

)로부터 다이아고날(diagonal) 성분이 제거된 행렬을 나타낸다. In the above equation,

And

Represents the common phase error caused by the phase noise of the i-th transmitting end and the j-th receiving end, respectively. Also,

And

Is the original matrix (

And

) ≪ / RTI > from which the diagonal components have been removed.

Meanwhile, the common phase error represented by the first term of Equation (17) can be compensated by the common phase error correction algorithm. For example, since the common phase error is the same phase error for every subcarrier, it can be predicted by deriving a solution of the Least-Square problem using pilots in the OFDM symbol, as described above .

Inter-carrier interference caused by phase noise is represented by the second term and the third term. Each term represents the inter-carrier interference caused by the phase noise of the receiving end and the transmitting end. Therefore, by simply expressing the inter-carrier-to-carrier interference, Equation (17) can be expressed by the following Equation (18).

은 수신단의 위상잡음으로 발생한 인접 캐리어간 간섭을 나타내며,

은 송신단의 위상잡음으로 발생한 인접 캐리어간 간섭을 나타낸다. In the above equation,

Represents inter-carrier interference caused by the phase noise of the receiving end,

Represents the inter-carrier interference caused by the phase noise of the transmitting end.

)을 산출하기 위해서, 프로세서(134)는 수신단 위상잡음으로 발생되는 인접 캐리어간 간섭(

)을 고려하여야 한다. 이를 위해, 프로세서(134)는

의 공분산 행렬(covariance matrix)를 산출한다. 이는 다음의 수학식 19 내지 21에 의해 산출될 수 있다. More precisely, the initial value of the transmission phase noise

The processor 134 calculates the inter-carrier interference (< RTI ID = 0.0 >

) Should be considered. To this end, the processor 134

Of the covariance matrix. This can be calculated by the following equations (19) to (21).

는 벡터의 평균 값을 뺀 나머지 벡터를 나타내며,

는

를 대각행렬(diagonal matrix)로 변환한 행렬을 나타낸다. 한편, D 는 다음의 수학식 20과 같이 나타날 수 있다. In the above equation,

Represents a residual vector obtained by subtracting the average value of the vector,

The

Into a diagonal matrix. On the other hand, D can be expressed by the following equation (20).

로 나타내면,

은 다음의 수학식 21에 의해 산출될 수 있다. On the other hand, in Equation (19), according to the assumption of the Wiener process for the formula analysis

Lt; / RTI >

Can be calculated by the following equation (21).

의 공분산 행렬을 이용하기 위해, 전술한 MAP 예측 기법을 변형하여

를 노이즈로 갖는 다음의 수학식 22을 통해 보다 정확한 송신단 위상잡음 초기값(

)을 산출할 수 있다. Therefore, the processor 134 calculates the values of < RTI ID = 0.0 >

In order to use the covariance matrix of

Phase noise initial value (< RTI ID = 0.0 >

) Can be calculated.

As described above, since the initial value must be obtained in a state in which the phase noise of the receiving end is unknown, the disclosed embodiment can estimate the initial value more accurately by calculating the transmitting end phase noise in consideration of the interference occurring in a situation where there is phase noise of the receiving end .

Further, as a further embodiment, the processor 134 may use interpolation to reduce complexity due to inverse matrix operations. For example, the processor 134 may reduce the size of the existing inverse matrix by M / N times using an interpolation matrix G of NxM (N> M) size. Accordingly, the above equations (11) and (12) can be expressed by the following equations (23) and (24).

와

은 인터폴레이션된 위상잡음의 공분산 행렬로서, 원래의 값을 알고 있다면 유도가능하다. In each of the equations (23) and (24)

Wow

Is a covariance matrix of interpolated phase noise, which is derivable if the original value is known.

Meanwhile, the phase error correction performance according to the result of removing the phase noise through the MIMO system 100 according to an embodiment of the present invention is shown in FIGS. 7 and 8. FIG.

FIG. 7 is a graph illustrating a phase error correction performance in a 2x2 MIMO system according to an embodiment of the present invention, and FIG. 8 is a graph illustrating a phase error correction performance in a 5x5 MIMO system according to an embodiment of the present invention.

을 적용하였다.7 and 8, the bit error rate (BER) performance is compared with the phase error correction algorithm according to an embodiment of the present invention and the conventional common phase error correction algorithm. At this time, as a condition simulating the phase noise elimination processing of the MIMO system 100, a system with 2X2 antennas and a system with 5X5 antennas were applied. As a modulation method, OFDM (FFT size: 64, pilot subcarrier: 16) and 16 QAM are used. As a phase noise model, Wiener Process

Respectively.

As described above, since the MIMO system 100 repeatedly estimates the phase noise, the initial transmission signal can be restored more accurately than the conventional technique in which only the common phase error is corrected, have.

As described above, the MIMO system 100 mathematically derives the phase noise of the transmitter and receiver based on the mathematical modeling of the system model in the presence of independent phase noise for each antenna stage. The phase error correction performance can be greatly improved by a conventional technique of correcting only the common phase error by processing an iterative algorithm. Also, if the number of pilots is larger than the number of antennas, a good performance can be obtained even in a multi-antenna system of 2X2 or more.

The method for removing phase noise of a MIMO system having an independent oscillator for each antenna according to an embodiment of the present invention may be implemented in the form of a recording medium including instructions executable by a computer such as a program module executed by a computer . Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. The computer-readable medium may also include computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

100: Multiple input / output system
110: antenna
120: Oscillator
131: MIMO-OFDM modulator
132: MIMO-OFDM demodulator
133: Memory
134: Processor

Claims (10)

A method for removing phase noise through a multiple input / output system having an independent oscillator for each antenna,
Receiving a transmission signal for data transmitted from a transmission antenna through a reception antenna and an oscillator;
Estimating a plurality of parameters for a phase noise of a transmitter and a phase noise of a receiver based on mathematical modeling of a signal transmitted and received through a MIMO system having independent oscillators for the antennas; And
And removing the phase noise of the transmitter and receiver predicted from the received signal,
Wherein the step of predicting the plurality of parameters comprises:
Calculating a plurality of relational expressions for the phase noise of the transmitter and the receiver; And
And stopping the iterative calculation and outputting the data calculated in the previous iterative step when the error value obtained according to the result of the iterative calculation is larger than the error value obtained in the previous iterative step. Way. The method according to claim 1,
The mathematical modeling for the multiple input /
Setting a log likelihood for the phase noise of the transmitting and receiving ends and performing a partial differentiation for each parameter to be predicted in the logarithmic likelihood function based on a MAP (Maximum A Posterior) prediction technique / RTI > 3. The method of claim 2,
The log likelihood function regarding the phase noise of the transmitting end and the receiving end is
The phase noise of the transmitting end and the phase noise of the receiving end are expressed by Equation (1) below.
[Equation 1]

, (

)

The phase noise of the transmitting end,

The phase noise of the receiving end,

Is the covariance matrix of the phase noise of the transmitter,

Is the covariance matrix of the phase noise of the receiving end, j is the identification number of the receiving antenna, y is the transmission signal,

Is a diagonal matrix of data vectors x,

The

F is a normalized DFT matrix, H is a diagonal matrix of

(At this time,

Represents the diagonal matrix of each channel). The method according to claim 1,
The step of repeatedly calculating the relational expressions for the phase noise of the transmitting and receiving ends,
Calculating a common phase error using a least square algorithm and calculating an initial value of the initial transmission signal in which the common phase error is corrected and phase noise of the transmitting terminal and the receiving terminal; And
Calculating a reception signal of a next iteration step after the phase error of the reception end is corrected based on an initial value of the phase noise of the transmission terminal and the reception terminal, processing the channel equalization in the frequency domain with respect to the reception signal of the next iteration step, And eliminating phase noise in the frequency domain with respect to a result of removing the channel influence and restoring the transmission signal of the next iteration step. 5. The method of claim 4,
The initial value of the phase noise of the transmitter
The initial transmission signal with the common phase error corrected

Is substituted into the following equation (2)
The initial value of the phase noise of the receiver
Wherein the phase noise canceling means is a solution obtained by substituting the initial transmission signal in which the common phase error is corrected and the solution of Equation (2) into the following Equation (3).
&Quot; (2) "

, (

)
&Quot; (3) "

, (

)
In the above equation,

Is an operator for converting a vector into a diagonal matrix, and represents an operator for converting matrices having the same size into a block diagonal matrix,

(

). 6. The method of claim 5,
The initial value of the phase noise of the transmitter
Wherein the phase noise canceling is a solution of Equation (4) taking into consideration interference between neighboring carriers due to the initial transmission signal in which the common phase error is corrected and the phase noise of the receiving end.
&Quot; (4) "

I is a vector composed of 1, Re {A} is an operator for extracting only a real part of A,

Is the noise variance,

Is an initial value of the phase noise of the receiving end,

Is the covariance matrix of the phase noise of the receiver,

Lt; RTI ID = 0.0 > inter-carrier interference,

The

Of the covariance matrix. The method according to claim 1,
The error value
And the difference between the result of the iterative operation and the value obtained using the pilot subcarrier. The method according to claim 1,
The step of predicting the plurality of parameters
Wherein the number of iterations is reduced using an interpolated matrix. In a multiple input / output system having an independent oscillator for each antenna,
A plurality of reception antennas;
A plurality of oscillators coupled to each of the plurality of receive antennas;
A memory for storing a program for removing phase noise of a receiving end and a transmitting end from a signal received via a receiving antenna and an oscillator; And
And a processor for executing the program,
The processor, as the program is executed,
A plurality of parameters for a phase noise of a transmitter and a phase noise of a receiver are predicted based on mathematical modeling of a signal transmitted and received through the MIMO system, And removing the phase noise of the receiving end,
The plurality of parameter estimates
And the relational expression for the phase noise of the transmitting end and the receiving end is alternately and repeatedly calculated. When the error value obtained according to the result of the repeated calculation is larger than the error value obtained in the previous iterative step, Outputting the data calculated in the iterative step. 9. A computer-readable recording medium having recorded thereon a program for performing the method according to any one of claims 1 to 8 on a computer.

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