KR102108852B1 - Energy efficient vector modulation method and apparatus for single-input multiple-output channels with low-resolution adcs - Google Patents

Abstract

Disclosed are a vector modulation method for improving energy efficiency in a single-input multiple-output antenna system using a low-resolution analog-to-digital converter and an apparatus thereof. According to one embodiment of the present invention, the vector modulation method, which is a vector modulation method in a single-input multiple-output antenna system, comprises the steps of: mapping transmission data to a low-density binary vector corresponding to the transmission data; and forming a transmission vector based on the mapped low-density binary vector and a predefined dictionary matrix. When forming the transmission vector, the transmission vector may be formed based on a low density superposition of the mapped low-density binary vector and dictionary vectors of the dictionary matrix.

Description

A vector modulation method and apparatus for increasing energy efficiency in a single input multiple output antenna system using a low resolution analog-to-digital converter.

The present invention relates to a vector modulation technique in a single input multiple output antenna system, and more particularly, to improve energy efficiency in a single input multiple output antenna system using a low-resolution analog-to-digital converter, and to reduce the complexity of data detection at the receiving end. It relates to a vector modulation method and apparatus that can be lowered.

Since the commercialization of 4G (4th-generation) wireless communication systems, the demand for high-speed transmission and reception of large amounts of data has increased significantly. Accordingly, research into 5G (5th-generation) wireless communication systems has been actively conducted to achieve higher throughput than 4G wireless communication. 5G wireless communication systems are being studied to achieve maximum speeds of 20Gbps and ultra-low latency of 1ms. In addition to the existing mobile communication system, this 5G technology is expected to be utilized in various fields such as the Internet of Things, virtual reality, and high-speed trains.

To achieve high data rates in 5G systems, the most common discussion is to use bandwidths in the hundreds of MHz in the ultrahigh frequency (mmWave) band. In the case of the ultra-high frequency signal, there is a disadvantage that it has a strong straightness and is vulnerable to fading. To compensate for this, 5G systems consider using much more base station antennas than in the existing 4G systems. However, when transmitting / receiving data at high speed using many antennas, it is problematic because the power consumed by the analog-to-digital converter (ADC) and digital-to-analog converter (DAC) is significant.

As a solution to this problem, a hybrid beamforming technique that combines analog phase conversion and digital beamforming through a small number of RF chains, low-resolution analog-to-digital converters, and digital A technique for reducing power consumption using an analog converter is typical.

On the other hand, when using a low-resolution analog-to-digital converter, there is a difficulty in that the entire system is expressed as nonlinear because the receiving end quantizes the signal with low resolution. In particular, in such a non-linear communication system, inter-subcarrier-interference cannot be eliminated by using an orthogonal frequency division (OFDM) technique that has been widely used.

Accordingly, there is a need for a modulation technique that has high energy efficiency and low data detection complexity at the receiving end in the case of using a low-resolution analog-to-digital converter.

SUMMARY Embodiments of the present invention provide a vector modulation method and apparatus for improving energy efficiency and reducing data detection complexity at a receiving end in a single input multiple output antenna system using a low resolution analog-to-digital converter.

A vector modulation method according to an embodiment of the present invention includes a vector modulation method in a single input multiple output antenna system, comprising: mapping transmission data to a low-density binary vector corresponding to the transmission data; And forming a transmission vector based on the mapped low-density binary vector and a predefined dictionary matrix.

In the forming of the transmission vector, the transmission vector may be formed based on a sparse superposition of the mapped low-density binary vector and the dictionary vectors of the dictionary matrix.

Furthermore, the vector modulation method according to an embodiment of the present invention may further include defining the dictionary matrix based on a preset channel value.

The step of defining the dictionary matrix defines the dictionary matrix such that the channel value has a preset restricted isometry property (RIP) coefficient when channel information is present, and an arbitrary orthogonal matrix when the channel information does not exist. Create, and define the dictionary matrix based on the generated orthogonal matrix.

In the step of defining the dictionary matrix, when the channel information exists, the dictionary matrix may be defined as a product of an inverse matrix and an orthogonal matrix of the channel matrix.

A data detection method according to an embodiment of the present invention, in a data detection method in a single input multiple output antenna system, receives a transmission signal including a transmission vector modulated based on a predefined dictionary matrix through a multiple reception antenna To do; Quantizing the received signal using a low-resolution analog-to-digital converter; And detecting transmission data corresponding to the transmission vector using the quantized reception signal and a preset algorithm.

In the detecting of the transmission data, the transmission data may be detected by detecting index information of dictionary vectors of the dictionary matrix.

The algorithm may include a LMP (Likelihood matching pursuit) algorithm.

A vector modulating apparatus according to an embodiment of the present invention includes a vector modulating apparatus in a single input multiple output antenna system, comprising: a mapping unit that maps transmission data into a low-density binary vector corresponding to the transmission data; And a modulator configured to form a transmission vector based on the mapped low-density binary vector and a predefined dictionary matrix.

The modulator may form the transmission vector based on the low-density superposition of the mapped low-density binary vector and the dictionary vectors of the dictionary matrix.

Furthermore, the vector modulation apparatus according to an embodiment of the present invention may further include a generator defining the dictionary matrix based on a preset channel value.

The generation unit defines the dictionary matrix such that the channel value has a preset restricted isometry property (RIP) coefficient when channel information is present, and generates an arbitrary orthogonal matrix when the channel information does not exist, and generates the channel information. The dictionary matrix may be defined based on the orthogonal matrix.

When the channel information is present, the generator may define the dictionary matrix by multiplying the inverse matrix and the orthogonal matrix of the channel matrix.

A data detection apparatus according to an embodiment of the present invention, in a data detection apparatus in a single input multiple output antenna system, receives a transmission signal including a transmission vector modulated based on a predefined dictionary matrix through a multiple reception antenna A receiving unit; A quantization unit for quantizing the received signal using a low-resolution analog-to-digital converter; And a detection unit detecting transmission data corresponding to the transmission vector using the quantized reception signal and a preset algorithm.

The detection unit may detect the transmission data by detecting index information of dictionary vectors of the dictionary matrix.

The algorithm may include a LMP (Likelihood matching pursuit) algorithm.

According to embodiments of the present invention, in a single input multiple output antenna system using a low-resolution analog-to-digital converter, energy efficiency may be improved and data detection complexity at a receiving end may be reduced.

That is, the present invention can provide a vector modulation technique in which energy efficiency and data detection complexity are considered in combination.

According to embodiments of the present invention, it is possible to achieve a lower error rate than known time domain or frequency domain modulation techniques because it can improve energy efficiency and lower data detection complexity.

1 shows a single input multiple output antenna system using a low resolution analog to digital converter for explaining the vector modulation method of the present invention.
2 shows an exemplary diagram for describing a process of modulating a transmission vector.
3 is a flowchart illustrating an operation of a vector modulation method according to an embodiment of the present invention.
4 is a flowchart illustrating an operation of a data detection method according to an embodiment of the present invention.
5 shows a configuration of a vector modulation apparatus according to an embodiment of the present invention.
6 shows a configuration of a data detection device according to an embodiment of the present invention.

Advantages and features of the present invention, and methods for achieving them will be clarified with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and common knowledge in the art to which the present invention pertains. It is provided to completely inform the person having the scope of the invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for describing the embodiments, and is not intended to limit the present invention. In the present specification, the singular form also includes the plural form unless otherwise specified in the phrase. As used herein, "comprises" and / or "comprising" refers to the components, steps, operations, and / or elements mentioned above of one or more other components, steps, operations, and / or elements. Presence or addition is not excluded.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as meanings commonly understood by those skilled in the art to which the present invention pertains. In addition, terms that are commonly defined in the dictionary are not ideally or excessively interpreted unless specifically defined.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. The same reference numerals are used for the same components in the drawings, and duplicate descriptions for the same components are omitted.

In the case of quantizing a signal through a low-resolution analog-to-digital converter, information of a transmission signal transmitted through a channel is greatly distorted. Accordingly, many errors occur in the process of detecting data. In order to minimize this, it is important to use channel diversity to the maximum.

The vector modulation technique according to embodiments of the present invention has the advantage that the diversity of channels can be maximized because transmission information is modulated over time or frequency resources.

When modulating the transmission information over the entire time and frequency resource, it has the advantage of being able to make the most of the diversity of channels, but from the perspective of the receiving end, it is optimal because all information is superimposed on each time and frequency signal. Optimal) requires a fairly high computational complexity in detecting data. Therefore, in the process of vector modulation of data, it is necessary to consider a method capable of lowering the data detection complexity.

The vector modulation technique according to embodiments of the present invention considers sparse combinations of dictionary vectors with low density in the process of vector modulation of data. In this case, detecting data is less overlapping. It is interpreted as a problem of detecting the index of a numerical dictionary vector, which can be solved with low complexity.

On the other hand, when the dictionary vectors are superimposed at a low density, the problem of finding the index of the superimposed dictionary vectors corresponds to a compressed sensing problem. Conventional compression sensing theory does not consider quantized signals through low-resolution analog-to-digital converters. In this case, many studies have been conducted on effective methods to find the indexes of dictionary vectors without error.

The technique according to the embodiments of the present invention can nonlinearly detect index information of dictionary vectors through low complexity in a case where information of a received signal is changed through low resolution quantization.

1 shows a single input multiple output antenna system using a low resolution analog to digital converter for explaining the vector modulation method of the present invention.

As shown in FIG. 1, the system includes a transmit end 110 and a receive end 120.

The transmitter 110 modulates the transmission data using the vector modulation method of the present invention and then transmits it through the transmission antenna.

Here, the transmitting terminal 110 maps the transmission data one-to-one with a corresponding low-density binary vector, and forms a low-density binary vector corresponding to the transmission data as a transmission vector through sparse superposition of predefined dictionary vectors. In this case, a transmission vector whose transmission data is modulated is transmitted through a transmission antenna. A detailed description of the process of forming such a transmission vector will be described in detail in FIG. 2.

The receiving end 120 includes a multiple receiving antenna 121, an RF chain 122, low resolution ADCs 123, and a receiving processing unit 124.

The multiple receive antenna 121 receives the signal from the air and provides it to the RF chain 122, and the output signal of the RF chain 122 is quantized through low resolution ADCs 123, for example, 1-bit ADCs. .

At this time, the multiple reception antenna 121 may receive a reception signal including a transmission vector transmitted from the transmission end.

Here, the RF chain 122 may be composed of various elements, and the RF chain 122 is a technical configuration known to those skilled in the art, and detailed description thereof will be omitted.

Low-resolution ADC 123 For example, a 1-bit ADC compares a signal output through the RF chain 122, that is, a preset threshold value for the magnitude of a received signal, such as a quantization value, for example, +1 or- The value of 1 is output.

Here, the 1-bit ADC 123 may include a 1-bit ADC that 1-bit quantizes the real part in the output signal of the RF chain 122 and a 1-bit ADC that 1-bit quantizes the imaginary part.

The reception processing unit 124 detects transmission data using a low-resolution quantized received signal output through the low-resolution ADC 123 and a preset algorithm, for example, a LMP (Likelihood matching pursuit) algorithm.

Here, the reception processing unit 124 may detect the transmission data by detecting the index information of the dictionary vectors of the dictionary matrix. In the present invention, the low-resolution ADC 123 was described as a 1-bit ADC, but the low-resolution ADC 123 in the present invention is not limited to a 1-bit ADC, and may include a low-resolution b-bit ADC. have. Here, when using a low-resolution b-bit ADC, when detecting transmission data, only the likelihood function of the LMP algorithm can be defined for the b-bit ADC. Therefore, the present invention is a low resolution ADC including a 1-bit ADC. Can be applied to all.

To further illustrate the present invention in such a system, the present invention superimposes a dictionary vector at a low density to increase energy efficiency and reduce data detection complexity in a single input multiple output antenna system using a low-resolution analog-to-digital converter. Or transmission data) in the time domain. Of course, the present invention is not limited to modulating data in the time domain by superimposing a dictionary vector at a low density in order to increase energy efficiency and lower data detection complexity, and data may be modulated in the frequency domain depending on the situation.

으로 표현한다. 또한 송신 안테나가 시간 영역 상에서 M번의 채널을 사용한 송신 벡터를

로 나타낼 수 있다. 이 경우 r번째 수신 안테나에서 수신하는 시간 영역 신호는 아래 <수학식 1>과 같이 나타낼 수 있다.The vector modulation method according to an embodiment of the present invention models a propagation channel as an L-tap inter-symbol-interference channel and performs block-by-block transmission at a transmitting end. Is assumed. In this embodiment, the interference channel between the transmit antenna and the r-th receive antenna

Is expressed as In addition, the transmit antenna uses a transmission vector using M channels in the time domain.

Can be represented as In this case, the time domain signal received from the r-th reception antenna may be expressed as Equation 1 below.

[Equation 1]

는 잡음 신호를 의미하는 것으로, 각 안테나와 각 시간에 대해 복소 정규분포(circularly-symmetric complex Gaussian)를 따를 수 있으며, 함수

는 연속적인 값을 분해능에 따라 양자화하는 아날로그디지털 변환을 나타내는 비선형 함수를 의미할 수 있다.here,

Is a noise signal. For each antenna and each time, a complex normal-symmetric complex Gaussian can be followed.

Can denote a nonlinear function representing an analog-to-digital conversion that quantizes continuous values according to resolution.

을 아래 <수학식 2>와 같이 정의하면 상기 수학식 1을 아래 <수학식 3>과 같이 표현할 수 있다.To represent Equation 1 as a determinant, a Toeplitz matrix

If is defined as <Equation 2> below, Equation 1 may be expressed as <Equation 3> below.

[Equation 2]

[Equation 3]

To express Equation 3 as a matrix equation of real coefficients, if a real coefficient vector and a matrix such as <Equation 4> below are defined, the matrix equations of <Equation 5> below can be obtained for each receive antenna.

[Equation 4]

[Equation 5]

In Equation 5, in order to represent all receive antennas in a matrix form, if vectors and matrices are concatenated as shown in <Equation 6> below, finally, the input-output relationship such as <Equation 7> is input. relation).

 [Equation 6]

[Equation 7]

는 아날로그디지털 변환기의 하드웨어에 따라 특성이 결정될 수 있다. 이러한 행렬 H, 벡터 v와 함수

는 송신단에서 다룰 수 없다.Here, the matrix H is characterized by the propagation channel situation, the vector v is determined by noise, and the function

The characteristic may be determined according to the hardware of the analog-to-digital converter. These matrices H , vectors v and functions

Cannot be handled by the transmitting end.

In this embodiment, an energy efficient vector modulation method for achieving a target frequency efficiency in a nonlinear input / output system represented by Equation (7) is provided. To this end, in this embodiment, the transmission vector x is modulated, which will be described with reference to FIG. 2.

2 shows an exemplary diagram for describing a process of modulating a transmission vector.

As shown in Fig. 2, the transmitting end includes a mapping means 111 and a modulation means 112.

The mapping means 111 maps the transmission data to be transmitted (information bits) on a one-to-one basis with a corresponding low-density binary vector.

The modulating means (sparse superposition vector modulation) 112 forms a transmission vector modulated in response to the transmission data (Tx) by forming a transmission vector based on a low-density binary vector corresponding to the transmission data and a predefined dictionary matrix. And transmits to the receiving end.

Here, the modulation means 112 may form a transmission vector through low-density superposition of the low-density binary vector and the dictionary vectors of the dictionary matrix.

Furthermore, the transmitting end may include generating means (not shown) for generating a dictionary matrix or storage means (not shown) for storing a pre-generated dictionary matrix.

2, the transmission data is mapped one-to-one with a corresponding low-density binary vector s .

At this time, the number of 1s in the low-density binary vector s is a total of K, and the lengths of the vectors s , N and K, can define the modulation level.

에 곱해져서 송신 벡터 x를 형성한다. 이는 송신 벡터 x가 행렬 D의 사전 벡터들의 저밀도 중첩(sparse superposition)을 통해 형성됨을 의미할 수 있으며, 아래 <수학식 8>과 같이 표현될 수 있다.The corresponding low-density binary vector s is a matrix

Multiplied by to form the transmission vector x . This may mean that the transmission vector x is formed through sparse superposition of dictionary vectors of the matrix D , and can be expressed as Equation 8 below.

[Equation 8]

Here, the number of modulated transmission signals or the target frequency efficiency may be represented by log (N choose K).

The following describes how to design a good dictionary matrix to improve the performance of a low-density superimposed vector modulation technique. According to the present invention, when Equation 8 is applied to Equation 7, in the case of vector modulation, Equation 9 below can be obtained.

[Equation 9]

As can be seen from Equation (9), Equation (9) can be interpreted as an effective channel value in HD when the vector s is viewed as an input.

In the present invention, the most important factor in reducing the error rate of the system is to set the dictionary matrix D. In the present invention, the influence of D can be interpreted in two ways.

First, the dictionary matrix D allows information of s to be divided over the entire time domain. As an extreme example, when there is no dictionary matrix (or D becomes an identity matrix), it can be expressed as Equation 10 below.

[Equation 10]

In the case of Equation 10 above, the vector s containing data is distributed only by a Toeplitz matrix (or multi-tap channel). In the case of s , there is a special structure in which the number of 1s is small, so that some of the received signals y do not contain any information about s .

Second, the dictionary matrix D has the effect of mixing each element of the actual propagation channel H. Thus, the effective channel for s HD has a structure of a sensing matrix suitable for compression sensing. Therefore, it is possible to effectively detect data with low complexity by using compression sensing theory in the process of detecting data.

The method of effectively defining the dictionary matrix D at the transmitting end is divided into when the channel information is known at the transmitting end and when there is no channel information. First, when the transmitter does not know the channel information, that is, when the channel information is not stored, one of the methods of effectively defining the matrix D is to generate and use an arbitrary orthogonal matrix. In the case of an orthogonal matrix, it is possible to perfectly satisfy the restricted isometry property (RIP), which determines the detection performance in compression sensing. When D is used, HD is likely to have a good RIP (restricted isometry property) coefficient, which may help improve the performance of the entire system. In addition, if there is no channel information, the dictionary matrix D may be defined as (Q | -Q) and sparsity constraint may be applied.

When the transmitter has channel information, that is, when storing channel information, when designing D , the effective channel HD is designed to have good RIP (restricted isometry property) coefficients, that is, preset RIP coefficients. For example, the matrix of D is expressed as the product of H ^{- 1} , the inverse matrix of H , and the FFT matrix F. In this case, the effective channel HD eventually becomes F. In the case of the Fourier matrix, the s vector is uniformly distributed over the entire time domain, and performance can be improved because RIP is perfectly satisfied as an orthogonal matrix. In addition, if there is channel information, the dictionary matrix D may be defined as a product of H ^{- 1} and (Q | -Q) and sparsity constraint may be applied. The present invention can improve performance by multiplying a matrix inverse and an orthogonal matrix of a channel when channel information is present. As described above, the present invention can improve performance by multiplying an inverse matrix of a channel and an orthogonal matrix (which may include all orthogonal matrices) when channel information is present.

가 정의될 수 있다.In general, in compression sensing, the characteristics of the sensing matrix dominate low-density signal recovery performance. At this time, as a coefficient for how good the characteristic of the sensing matrix A is

Can be defined.

가 존재하여 아래 <수학식 11>을 만족시킬 때, 행렬 A가 상수

를 가지는 s-RIP를 만족시킨다.Coefficients for all matrices As obtained by selecting any s columns of matrices A

When <Equation 11> below satisfies the condition, matrix A is a constant

Satisfies s-RIP.

[Equation 11]

가 직교행렬인 경우, 무작위로 임의의 열을 선택하여도, 얻은 As의 열들이 각각 수직이기 때문에 상수(constant)

=0 로 isometry property를 만족하게 된다. 하지만 일반적으로 A가 정사각행렬이 아니며, 열의 개수가 행의 개수보다 많은 fat matrix인 경우, s개의 열을 선택하여 얻어질 수 있는 As들 중에서 각 열들이 수직을 이루지 않는 경우가 반드시 발생한다. 따라서 fat matrix A는 다른 조건이 없을 때 상수

=0로 RIP를 만족할 수 없다. A를 직교행렬을 두는 것은 간단하지만 높은 주파수 효율을 달성하지 못한다는 점에 한계가 있다. 직교행렬로부터 얻을 수 있는 As의 가지 수는 총

에 불과하다.The simplest example of RIP, A

When is orthogonal matrix, even if random columns are randomly selected, constants are obtained because the columns of As obtained are vertical.

The isometry property is satisfied by = 0. However, in general, when A is not a square matrix and the number of columns is a fat matrix having more than the number of rows, it is necessary that each column is not vertical among As obtained by selecting s columns. So fat matrix A is constant when there are no other conditions

= 0, the RIP cannot be satisfied. It is simple to put A in an orthogonal matrix, but there is a limitation in that it cannot achieve high frequency efficiency. The number of branches of As that can be obtained from an orthogonal matrix is

It is only.

=0로 RIP를 만족하게 하는 것이 가능하다. 임의의 직교 행렬

에 대해 이를 확장한 행렬 A

를

로 정의한다. 행렬 A에서 s개의 열을 선택하여 As를 형성할 때, A의 i번째 열이 선택되었으면, n+i (mod 2n) 번째 열은 선택되지 않는다고 가정하면(sparsity constraint), 이 조건 하에서 행렬 As에는 부호만 다른 두 열 벡터가 절대 포함될 수 없다. 즉 As의 각 열들은 항상 수직이 된다. 따라서 A가 상수

=0으로 RIP를 만족하게 된다. 이 경우 얻을 수 있는 As의 가지 수는 총

가지로서, A를 직교 행렬로 두는 때 보다 훨씬 더 높은 (s bit higher) 주파수 효율을 달성할 수 있다.However, if A is a fat matrix and the number of columns does not exceed two times the number of rows, A is added to add a special condition.

It is possible to satisfy RIP with = 0. Random orthogonal matrix

Matrix A that extends it for

To

Is defined as When forming As by selecting s columns from matrix A, assuming that the i-th column of A is selected, the n + i (mod 2n) -th column is not selected (sparsity constraint). Two-column vectors with only different signs can never be included. That is, each column of As is always vertical. Therefore, A is a constant

= 0 to satisfy RIP. In this case, the number of branches of As can be obtained

As a branch, it is possible to achieve s bit higher frequency efficiency than when A is placed in an orthogonal matrix.

The signal modulated by this process can be detected through a low complexity compression sensing technique. For example, since the s vector enters the received signal through the low-resolution ADC over the entire time, it is not practical because exponential complexity increases when using the optimal detection method (maximum likelihood). Therefore, the present invention can detect it through the LMP algorithm. That is, the receiving end can detect the transmission data using the quantization value and the LMP algorithm quantized by the low-resolution ADC, and preferably, by detecting the index information of the dictionary vectors of the dictionary matrix included in the transmission vector, Can be detected.

Here, the LMP may have a characteristic that the s vector that detects differently from the compression sensing algorithms in the case of using the conventional compression sensing and low resolution ADC is a binary vector, and unlike such conventional compression sensing algorithms, use this discrete characteristic. Therefore, it can show better performance than the existing algorithms for finding continuous signals.

The key idea of LMP is to continuously identify the elements of a support set that generates ML values. The LMP algorithm is explained as follows.

라 가정하면, LMP 알고리즘은 K-저밀도 정보 벡터를 검출하기 위하여 K 번 반복 수행한다. 각 반복에서, 지원 셋의 요소를 식별한다. 본 발명은 k번째 반복까지 추정된 지원 셋을

로 나타낸다.Real support three of low density information vector s

Suppose, LMP algorithm iterates K times to detect K-low density information vector. In each iteration, elements of the support set are identified. The present invention provides an estimated set of support up to the kth iteration.

It is represented by.

는 sn=1인 전제하에 아래 <수학식 12>와 같이 나타낼 수 있다. 여기서,

일 수 있다.In the first iteration, the LMP algorithm computes N likelihood functions on the assumption that each element of s is nonzero. Then, find the support element with the maximum likelihood value. To this end, the present invention is a received signal

Can be expressed as <Equation 12> below under the assumption that sn = 1. here,

Can be

[Equation 12]

는 유효 노이즈 벡터를 의미할 수 있다.here,

Can mean an effective noise vector.

로

의 분포를 모델링할 수 있다. 여기서,

는 대각선 요소들을 갖는 대각선 행렬을 의미할 수 있으며, 대각선 요소들

는 아래 <수학식 13>과 같이 나타낼 수 있다.In order to design the LMP algorithm, the present invention

in

You can model the distribution of. here,

Can mean a diagonal matrix with diagonal elements, and diagonal elements

Can be expressed as <Equation 13> below.

[Equation 13]

일 수 있다.here,

Can be

The first item of Equation (12) represents the uncertainty of the support set, and the second item represents the uncertainty of the noisy. Using the Gaussian noise model, the log-likelihood function can be approximated as shown in <Equation 14> below under the assumption of sn = 1.

[Equation 14]

When the log likelihood value is calculated, the LMP algorithm updates the best likelihood position of s with the elements of the support set. At this time, the elements of the support set can be expressed as <Equation 15> below.

[Equation 15]

까지 이전에 식별된 지원 셋을 이용하여 sn=1의 전제하에서 k번째 반복에서의 로그 우도 함수를 아래 <수학식 16>과 같이 나타낼 수 있다.In the second iteration, the LMP algorithm uses the previously detected support as additional information to find a support element. until k-1 iteration, i.e.

The log likelihood function at the k-th iteration under the premise of sn = 1 can be expressed as <Equation 16> below using the previously identified support set.

[Equation 16]

의 가정하에서 상기 <수학식 12>를 아래 <수학식 17>과 같이 나타낼 수 있다.To calculate the log likelihood function, the present invention

Under the assumption of <Equation 12> can be expressed as <Equation 17> below.

[Equation 17]

A summary of the LMP algorithm can be expressed as Algorithm 1 below.

는 sn=1의 전자하에서 k번째 반복의 유효 노이즈 벡터를 의미할 수 있다.here,

Can mean the effective noise vector of the k-th iteration under the electron of sn = 1.

의 분포를

로 가정할 때, 대각선 요소들

는 아래 <수학식 18>과 같이 나타낼 수 있다.Like the first iteration, the present invention is an effective noise vector.

The distribution of

Assuming that, diagonal elements

Can be expressed as <Equation 18> below.

[Equation 18]

Using Equation 16 and Equation 17, the log likelihood function at the k-th iteration may be approximated as shown in Equation 19 below.

[Equation 19]

When the likelihood value is calculated, LMP detects the elements of the support set as shown in Equation 20 below.

[Equation 20]

로 업데이트하고, 이러한 반복적인 과정은 조건

이 만족될 때까지 수행된다.LMP algorithm supports three

Updated, and these iterative processes are conditional

This is done until satisfied.

3 illustrates an operation flowchart for a vector modulation method according to an embodiment of the present invention, and illustrates an operation flowchart at the above-described transmitting end.

Referring to FIG. 3, a vector modulation method according to an embodiment of the present invention includes defining a dictionary matrix based on an effective channel value (S310) and mapping transmission data to a low-density binary vector corresponding to transmission data (S320) And forming a transmission vector or modulating the transmission vector based on the mapped low-density binary vector and the defined dictionary matrix (S330).

In step S310, if the channel information exists, that is, when the transmitting end can know the channel information, a dictionary matrix is defined so that the effective channel value has a preset RIP (restricted isometry property) coefficient, and if the channel information does not exist, that is, , If the channel information is unknown at the transmitting end, an arbitrary orthogonal matrix may be generated and then a dictionary matrix may be defined based on the generated orthogonal matrix.

Here, in step S310, when the channel information exists, a dictionary matrix may be defined as a product of an inverse matrix of a channel matrix and an orthogonal matrix, for example, a Fourier matrix.

That is, step S310 is a process of designing and generating a dictionary matrix so that data can be effectively detected with low complexity in the process of detecting data at the receiving end. The dictionary matrix divides the low-density binary vector over the entire time domain, and propagates in real time. It functions to mix each element of the channel.

In step S320, the transmission data to be transmitted is mapped one-to-one to a corresponding low-density binary vector.

Step S330 forms a transmission vector through a low-density binary vector and a sparse superposition of dictionary vectors of a dictionary matrix. The transmission vector thus formed is transmitted through a transmission antenna.

4 shows an operation flowchart for a data detection method according to an embodiment of the present invention, and shows an operation flowchart at the above-described receiving end.

Referring to Figure 4, the data detection method according to an embodiment of the present invention comprises the steps of receiving a received signal including a transmission vector through a multiple receive antenna (S410), a low-resolution ADC for the received received signal, for example, 1 Quantization using a bit ADC (S420) and detecting the transmission data using a preset algorithm and a received signal quantized with a low resolution (S430).

In step S410, the transmitting end receives a signal including a transmission vector formed by a dictionary vector using a multiple reception antenna, and the received signal may be received as shown in Equation 10 above.

Of course, the received signal received through the multiple receive antenna can be provided to the low-resolution ADC through the RF chain.

Step S420 quantizes each of the real and imaginary parts separated through the RF chain using an ADC that quantizes the real part with low resolution and an ADC that quantizes the imaginary part with low resolution.

Step S430 detects the transmission data by detecting the index information of the dictionary vectors of the dictionary matrix using a low-resolution quantized received signal output through a low-resolution ADC and a preset algorithm, for example, a LMP (Likelihood matching pursuit) algorithm. Can be detected.

Here, the LMP algorithm may be characterized as a low-density binary vector that detects differently from the compression sensing algorithms in the case of using the existing compression sensing and low-resolution ADCs, and, unlike the existing compression sensing algorithms, uses this discrete characteristic. It can show better performance than the existing algorithms for finding continuous signals.

5 shows a configuration for a vector modulation device according to an embodiment of the present invention, and shows a configuration for a device included in a transmitting end.

5, the vector modulation apparatus 500 according to an embodiment of the present invention includes a generator 510, a mapping unit 520, a modulator 530 and a transmitter 540.

The generator 510 defines a dictionary matrix based on the effective channel value.

Here, the generator 510 defines a dictionary matrix so that the effective channel value has a preset RIP (restricted isometry property) coefficient when the channel information exists, that is, when the transmitting end can know the channel information, the channel information If it does not exist, that is, if the channel information is unknown at the transmitting end, an arbitrary orthogonal matrix may be generated and then a dictionary matrix may be defined based on the generated orthogonal matrix.

In this case, the generator 510 may define a dictionary matrix by multiplying an inverse matrix of a channel matrix and an orthogonal matrix, for example, a Fourier matrix, when channel information is present.

The mapping unit 520 is configured to correspond to the mapping means 111 shown in FIG. 2, and maps the transmission data into a low-density binary vector corresponding to the transmission data.

Here, the mapping unit 520 may map the transmission data to be transmitted one-to-one with a corresponding low-density binary vector.

The modulator 530 has a configuration corresponding to the modulating means 112 shown in FIG. 2, and modulates the transmission vector into a transmission vector or a transmission vector based on the mapped low-density binary vector and the defined dictionary matrix.

Here, the modulator 530 may form a transmission vector through low-density superposition of the low-density binary vector and the dictionary vectors of the dictionary matrix.

The transmitting unit 540 includes a transmitting antenna, and transmits a transmission vector modulated by the modulating unit to the receiving end through the transmitting antenna.

6 illustrates a configuration of a data detection device according to an embodiment of the present invention, and shows a configuration of a device included in a receiving end.

Referring to FIG. 6, the data detection apparatus 600 according to an embodiment of the present invention includes a reception unit 610, a quantization unit 620, and a detection unit 630.

The reception unit 610 corresponds to a configuration including the multiple reception antenna 121 of FIG. 1 and the RF chain 122 according to circumstances, and receives a reception signal including a transmission vector through the multiple reception antenna, and the RF chain It is provided to the quantization unit through.

The quantization unit 620 is configured to correspond to the low-resolution ADC 123 illustrated in FIG. 1, and quantizes the received received signal using a low-resolution ADC, for example, a 1-bit ADC.

Here, the quantization unit 620 may quantize each of the real and imaginary parts separated through the RF chain using an ADC that quantizes the real part with low resolution and an ADC that quantizes the imaginary part with low resolution.

The detection unit 630 is configured to correspond to the receive processing 124 shown in FIG. 1, and detects transmission data using a low resolution quantized reception signal and a preset algorithm.

Here, the detector 630 detects index information of dictionary vectors of the dictionary matrix by using a received signal quantized with a low resolution output through a low resolution ADC and a preset algorithm, for example, a LMP (Likelihood matching pursuit) algorithm. , Transmission data can be detected.

Although the description of the devices of FIGS. 5 and 6 is omitted, those skilled in the art may appreciate that the devices of FIGS. 5 and 6 may include all the functions described in the systems and methods of FIGS. 1 to 4. It is self-evident.

The system or device described above may be implemented with hardware components, software components, and / or combinations of hardware components and software components. For example, the systems, devices, and components described in the embodiments include, for example, processors, controllers, arithmetic logic units (ALUs), digital signal processors (micro signal processors), microcomputers, and field programmable arrays (FPAs). ), A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose computers or special purpose computers. The processing device may run an operating system (OS) and one or more software applications running on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, a processing device may be described as one being used, but a person having ordinary skill in the art, the processing device may include a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that may include. For example, the processing device may include a plurality of processors or a processor and a controller. In addition, other processing configurations, such as parallel processors, are possible.

The software may include a computer program, code, instruction, or a combination of one or more of these, and configure the processing device to operate as desired, or process independently or collectively You can command the device. Software and / or data may be interpreted by a processing device, or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodied in the transmitted signal wave. The software may be distributed over networked computer systems, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

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

As described above, although the embodiments have been described by a limited embodiment and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques are performed in a different order than the described method, and / or the components of the described system, structure, device, circuit, etc. are combined or combined in a different form from the described method, or other components Alternatively, even if replaced or substituted by equivalents, appropriate results can be achieved.

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

Claims (16)

In the vector modulation method in a single input multiple output antenna system,
Mapping transmission data to a low-density binary vector having a preset number of '1' corresponding to the transmission data; And
Forming a transmission vector based on a sparse superposition overlapping dictionary vectors, which are vectors constituting a predefined dictionary matrix, based on the mapped low-density binary vector and a preset channel value.
A vector modulation method comprising a.
delete According to claim 1,
Defining the dictionary matrix based on a preset channel value
A vector modulation method further comprising a.
According to claim 3,
The step of defining the dictionary matrix is
When the channel information exists, the dictionary matrix is defined so that the channel value has a preset restricted isometry property (RIP) coefficient,
A vector modulation method comprising generating an arbitrary orthogonal matrix when the channel information does not exist and defining the dictionary matrix based on the generated orthogonal matrix.
According to claim 3,
The step of defining the dictionary matrix is
A vector modulation method characterized by defining the dictionary matrix by multiplying an inverse matrix of a channel matrix corresponding to the channel information and a predefined orthogonal matrix when channel information exists.
A method for detecting data in a single input multiple output antenna system,
Receiving a transmission signal including a transmission vector modulated based on a predefined dictionary matrix based on a preset channel value through a multiple reception antenna;
Quantizing the received signal using a low-resolution analog-to-digital converter; And
Detecting transmission data corresponding to the transmission vector using the quantized reception signal and a preset algorithm
Data detection method comprising a.
The method of claim 6,
The step of detecting the transmission data is
And detecting the transmission data by detecting index information of dictionary vectors, which are vectors constituting the dictionary matrix.
The method of claim 6,
The algorithm is
And a LMP (Likelihood matching pursuit) algorithm.
A vector modulation device in a single input multiple output antenna system,
A mapping unit for mapping transmission data to a low-density binary vector in which the number of '1' corresponding to the transmission data is a preset number; And
Modulation to form a transmission vector based on a sparse superposition overlapping dictionary vectors, which are vectors constituting a predefined dictionary matrix, based on the mapped low-density binary vector and a preset channel value. part
A vector modulation device comprising a.
delete The method of claim 9,
A generator for defining the dictionary matrix based on a preset channel value
A vector modulation device further comprising a.
The method of claim 11,
The generating unit
When the channel information exists, the dictionary matrix is defined so that the channel value has a preset restricted isometry property (RIP) coefficient,
If the channel information does not exist, a vector modulation apparatus characterized in that it generates an arbitrary orthogonal matrix and defines the dictionary matrix based on the generated orthogonal matrix.
The method of claim 11,
The generating unit
When the channel information is present, a vector modulation apparatus characterized in that the dictionary matrix is defined as a product of an inverse matrix of a channel matrix corresponding to the channel information and a predefined orthogonal matrix.
In the data detection device in a single input multiple output antenna system,
A receiver configured to receive a transmission signal including a transmission vector modulated based on a predefined dictionary matrix based on a preset channel value through a multiple reception antenna;
A quantization unit for quantizing the received signal using a low-resolution analog-to-digital converter; And
A detector for detecting transmission data corresponding to the transmission vector using the quantized received signal and a preset algorithm
Data detection device comprising a.
The method of claim 14,
The detection unit
And detecting the transmission data by detecting index information of dictionary vectors, which are vectors constituting the dictionary matrix.
The method of claim 14,
The algorithm is
Data detection apparatus comprising a LMP (Likelihood matching pursuit) algorithm.

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