KR101399038B1 - Method and apparatus of signal estimation of second-order cyclostationary signal - Google Patents

Method and apparatus of signal estimation of second-order cyclostationary signal Download PDF

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KR101399038B1
KR101399038B1 KR1020130037538A KR20130037538A KR101399038B1 KR 101399038 B1 KR101399038 B1 KR 101399038B1 KR 1020130037538 A KR1020130037538 A KR 1020130037538A KR 20130037538 A KR20130037538 A KR 20130037538A KR 101399038 B1 KR101399038 B1 KR 101399038B1
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vector
frequency domain
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received signal
signal
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여정호
조준호
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포항공과대학교 산학협력단
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
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    • H04L25/021—Estimation of channel covariance

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Abstract

A method for estimating a signal according to the present invention comprises the steps of: sampling a received signal by a regular cycle and generating a received signal vector; generating a frequency domain vector for the received signal vector by using the received signal vector and a discrete Fourier transform of a complex conjugate of the received signal vector; generating a first modified frequency domain vector based on the frequency domain vector; approximating a covariance matrix of a second modified frequency domain vector related to a transmitted signal vector based on a second-order cyclostationary signal characteristic; and estimating an estimator of the transmitted signal vector by using the approximated covariance matrix and the first modified frequency domain vector. Therefore, the present invention reduces a calculation amount rather than a traditional method and attains close to optimal performance when performing a signal process like signal estimation.

Description

Field of the Invention [0001] The present invention relates to a method and an apparatus for estimating a signal using a secondary cyclic normal signal characteristic,

The present invention relates to a wireless communication system, and more particularly, to a method and apparatus for estimating a second-order cyclostationary signal.

The present invention can be applied to a communication and signal processing system, such as a cellular system, a relay system, an ad hoc network, and a technology related to wireless cognitive communication and received signal processing. Research on efficient signal processing techniques in communication and signal processing systems has been carried out for a long time. And to achieve maximum performance with low complexity.

Many digital signals are modeled as a regular periodic random process of light. This means that the average of the random process and the auto-correlation function have periodicity. In a complex random process, a complementary auto-correlation function (or a pseudo auto-correlation function) is always called a proper. Although many digital signals are proper-complex, signals such as pulse amplitude modulation (PAM), staggered quaternary phase-shift keying (SQPSK), and Gaussian minimum shifts are also useful for complementary autocorrelation functions with periodicity improper Signal.

Conventionally, a conventional linear signal processing method for achieving high performance for an inappropriate signal has been a linear-conjugate linear method or a widely linear method. These methods consist of two parts, one directly processing the signal linearly and one processing the conjugate complex of the signal linearly. Two linear systems must be designed.

A linear-conjugated linear method or a broad linear method can also be applied to the method of sampling and processing these signals and then processing them as a vector. This also consists of a portion that processes the signal vector directly and a portion that processes the complex conjugate of the signal vector. To do this, two matrices must be designed. In such a vector signal processing, a method of substantially equalizing the optimal method and performance by using the quadratic periodicity and having a much lower complexity is required.

An object of the present invention to solve the above problems is to provide a signal processing method using a quadrature periodicity of a signal and having a performance which is optimal and whose complexity is much lower.

According to another aspect of the present invention, there is provided a method of estimating a transmission signal using a received signal, the method including: sampling the received signal at a predetermined period to generate a received signal vector; Generating a frequency domain vector of the received signal vector using a complex conjugate discrete Fourier transform of the vector, generating a first modified frequency domain vector based on the frequency domain vector, Estimating a covariance matrix of the transformed frequency domain vectors based on the quadratic period normal signal characteristic and estimating a transmitter signal vector estimator using the approximated covariance matrix and the first transformed frequency domain vector can do.

According to another aspect of the present invention, there is provided an apparatus for estimating a transmission signal using a reception signal, the apparatus comprising: a sampling unit for sampling the reception signal at a predetermined period to generate a reception signal vector; A frequency domain vector generating unit for generating a frequency domain vector of the received signal vector using a discrete Fourier transform of a complex conjugate of a signal vector, a first transformed frequency domain generating a first transformed frequency domain based on the frequency domain vector, An approximation unit for approximating a covariance matrix of a second modified frequency domain vector related to the transmission signal vector, and an estimator for a transmission signal vector using the approximated covariance matrix and the first modified frequency domain vector, And an estimating unit that estimates the estimated value.

According to the signal estimation method and apparatus using the quadratic periodic normal signal characteristic of the present invention, when performing signal processing such as signal estimation, there is an effect of achieving near optimal performance while reducing the amount of computation much more than the conventional method.

1 is a block diagram schematically showing a configuration of a signal estimating apparatus according to an embodiment of the present invention;
2 is a detailed block diagram specifically illustrating a conversion performing unit according to an embodiment of the present invention,
3 is a detailed block diagram illustrating a covariance matrix approximation unit according to an exemplary embodiment of the present invention.
4 is a flowchart schematically illustrating a signal estimation method according to an embodiment of the present invention,
FIG. 5 is a graph showing a calculation amount of a signal estimating apparatus according to an embodiment of the present invention,
6 is a graph illustrating a mean square error of a signal estimator according to an embodiment of the present invention.

While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail.

It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

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

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to facilitate the understanding of the present invention, the same reference numerals are used for the same constituent elements in the drawings and redundant explanations for the same constituent elements are omitted.

An apparatus and method for estimating a signal according to an embodiment of the present invention may use an estimator of a quadratic period normal signal characteristic.

The covariance matrix of the second period normal signal vector is a block Toeplitz matrix, and a covariance matrix of the frequency signal component vector can be obtained by multiplying the discrete Fourier transform matrix by a backward discrete Fourier transform matrix before the covariance matrix. At this time, the covariance matrix of the frequency signal component vector can be approximated by a block matrix in which each element is composed of diagonal matrices. Also, the complementary covariance matrix of the quadratic normal signal vector is also a block-to-wavelet matrix.

According to an embodiment of the present invention, the characteristics of such a secondary periodic normal signal vector can be used to transform the signal vector into an equivalent but approximately approximate frequency domain signal vector. In addition to this transformation, the signal estimation is performed by approximating a covariance matrix of equivalent frequency domain signal vectors to a block matrix composed of diagonal blocks. The signal estimating apparatus and method using the proposed method according to the present invention, And has almost the same performance as the estimation method.

1 is a block diagram schematically illustrating a configuration of a signal estimating apparatus according to an embodiment of the present invention. 1, a signal estimating apparatus according to an embodiment of the present invention includes a receiving unit 110, a sampling unit 120, a conversion performing unit 130, a covariance matrix approximating unit 140, and an estimating unit 150 ).

Referring to FIG. 1, a receiving unit 110 receives a received signal z (t). For example, if the received signal vector is z, the signal vector to be estimated is x, and the noise signal vector is w, the signal model is as follows.

Figure 112013029909827-pat00001

It may be considered to perform M times oversampling on the secondary cyclic normal signal transmitted in the signal model as shown in Equation (1), and to sample by N symbol length.

That is, the sampling unit 120 suitably samples the signal received by the receiving unit 110 to generate a received signal vector. At this time, the length of the received signal vectors is MN. Hereinafter, a covariance matrix of the transmission signal x is defined as a complementary covariance matrix of R x , x

Figure 112013029909827-pat00002
. Depending on the characteristics of the normal periodic signal, R x and
Figure 112013029909827-pat00003
Becomes a block-to-sum matrix. The entropy vector w is an appropriate complex Gaussian vector with an average of zero and a covariance matrix σ 2 I MN . That is,
Figure 112013029909827-pat00004
. Accordingly, the covariance matrix and the complementary covariance matrix of the received signal vector z and is Rz = Rx + σ 2 I MN, respectively
Figure 112013029909827-pat00005
. Here, I MN is a MN × MN unit matrix.

In the signal model as shown in Equation (1), the linear predictor of the conventional optical system can be expressed as follows.

Figure 112013029909827-pat00006

Here, F1 and F2 can be defined by the following equations (3) and (4).

Figure 112013029909827-pat00007

Figure 112013029909827-pat00008

In the linear estimation method of the conventional optical system as shown in Equation (2), F 1 and F 2 must be designed and the secondary periodicity is not used. A high computational load may be required in this process. Therefore, the signal estimating apparatus according to an embodiment of the present invention can appropriately transform in the frequency domain and approximate the covariance matrix of the transformed vector, thereby greatly reducing the amount of computation. Moreover, the performance is almost the same.

The conversion performing unit 130 converts the received signal vector sampled by the sampling unit 120 into a frequency domain. At this time, a center discrete Fourier transform having a size N can be used.

FIG. 2 is a detailed block diagram specifically illustrating a conversion performing unit 130 according to an embodiment of the present invention. Referring to FIG. 2, the transform performing unit 130 may include a frequency domain vector generating unit 132 and a modified frequency domain vector generating unit 134.

Referring to FIG. 2, the frequency domain vector generating unit 132 converts the sampled received signal vector into a frequency domain vector using discrete Fourier transform. At this time, the (m, n) th component of the used center discrete Fourier transform matrix W N can be defined as follows.

Figure 112013029909827-pat00009

here,

Figure 112013029909827-pat00010
to be. According to another embodiment of the present invention, the frequency domain vector generating unit 132 may estimate a certain c N by a similar process in addition to the central Fourier transform defined by Equation (5).

The frequency domain vector generation unit 132 transforms the complex conjugate of the received signal vector and the received signal vector into the frequency domain using Equation 5,

Figure 112013029909827-pat00011
Lt; / RTI > The frequency domain vector
Figure 112013029909827-pat00012
Can be defined as follows.

Figure 112013029909827-pat00013

In addition, the estimated transmission signal vector and the complex conjugate thereof can be transformed into the frequency domain, and the frequency domain vector of the coupled transmission signal vector can be defined as follows.

Figure 112013029909827-pat00014

here,

Figure 112013029909827-pat00015
To
Figure 112013029909827-pat00016
, The following equation
Figure 112013029909827-pat00017
Can be expressed as follows.

Figure 112013029909827-pat00018

Then, the noise vector and its complex conjugate are transformed into the frequency domain,

Figure 112013029909827-pat00019
Is defined as follows.

Figure 112013029909827-pat00020

The frequency domain vector generation unit 132 generates a frequency domain vector

Figure 112013029909827-pat00021
The modified frequency domain vector generating unit 134 multiplies the following matrix by the following matrix to cut out unnecessary components to generate a modified frequency domain vector of the received signal vector.

Figure 112013029909827-pat00022

Where O N / 2 is a N / 2 × N / 2 zero matrix,

Figure 112013029909827-pat00023
Means a Kronecker product. When the matrix defined by Equation (10) is multiplied by the frequency domain vector of the received signal vector, the following deformation frequency signal model can be obtained.

Figure 112013029909827-pat00024

In this case, although it is expressed by multiplying by G,

Figure 112013029909827-pat00025
,
Figure 112013029909827-pat00026
And
Figure 112013029909827-pat00027
Since it repeats the process of deleting and leaving N / 2 components alternately in the vector, it can be calculated without performing operations such as addition or multiplication.

Referring back to FIG. 1, after the modified frequency domain vector generation unit 134 generates the modified frequency domain vector, the covariance matrix approximation unit 140 approximates the covariance matrix of the modified frequency domain vector of the transmitted signal vector. This will be described with reference to FIG.

FIG. 3 is a detailed block diagram illustrating a covariance matrix approximation unit 140 according to an embodiment of the present invention. 3, the covariance matrix approximation unit 140 according to an exemplary embodiment of the present invention may include a covariance matrix acquisition unit 142 and an approximation unit 144. [

Referring to FIG. 3, the covariance matrix obtaining unit 142 obtains a transformed frequency domain vector

Figure 112013029909827-pat00028
Of the covariance matrix. Then, the approximation unit 144 approximates the covariance matrix using the following equation.

Figure 112013029909827-pat00029

here,

Figure 112013029909827-pat00030
Denotes a covariance matrix of a transformed frequency domain vector of the transmission signal vector, and ⊙ denotes a Hadamed product. This approximation is possible because the covariance matrix of the quadratic normal signal vector is a block topolitz matrix, and if the covariance matrix is multiplied by a discrete Fourier transform matrix and then by an inverse discrete Fourier transform matrix, a covariance matrix of the frequency signal component vector is obtained It is because. Also, the complementary covariance matrix is also a block-to-wavelet matrix, which can also use the characteristics of a quadratic periodic normal signal vector, so that the signal vector can be converted into an equivalent, . Furthermore,
Figure 112013029909827-pat00031
Is a suitable complex random vector, it can be expressed by MN 占 MN zero matrix
Figure 112013029909827-pat00032
Lt; RTI ID = 0.0 > covariance matrix. ≪ / RTI > if,
Figure 112013029909827-pat00033
Is an inadequate complex random vector for a limited observation period,
Figure 112013029909827-pat00034
Can be almost optimally processed by utilizing a covariance matrix for a sufficiently large interval.

1, after the approximation of the covariance matrix of the transformed frequency domain vector with respect to the transmit signal vector is completed, the estimator 150 uses the approximated covariance matrix and the transformed frequency domain vector of the received signal vector to calculate the transmit signal vector .

Here, the presumptions mentioned are as follows.

Figure 112013029909827-pat00035

here,

Figure 112013029909827-pat00036
Represents a linear predictor of the transformed frequency domain vector for the transmit signal vector. The estimating unit 150 calculates a linear predictor
Figure 112013029909827-pat00037
And then,
Figure 112013029909827-pat00038
And then using the inverse centered discrete Fourier transform to obtain a predictor of the transmitted signal vector x after taking a partial complex conjugate. This transformation is preceded by a modified frequency domain vector x
Figure 112013029909827-pat00039
By performing a precise inverse operation of the conversion process.

4 is a flowchart schematically illustrating a signal estimation method according to an embodiment of the present invention.

Referring to FIG. 4, when the signal estimating apparatus receives a received signal (S410), the received signal is sampled at regular intervals to obtain a sampled signal, and a frequency signal vector is generated from the sampled signal (S420). Here, sampling can use oversampling, and the generated frequency signal vector is used for signal estimation. When generating the frequency signal vector, a centered discrete Fourier transform is used, but any discrete Fourier transform may be used. In operation S430, the modified frequency domain vector is generated by multiplying the generated frequency signal vector by a G matrix (see Equation 10) to cut unnecessary components. Then, the covariance matrix information of the transformed frequency domain vector of the transmission signal vector is obtained (S440). The covariance matrix is approximated (S450). Then, the signal estimator estimates a linear predictor of a transmission signal vector using an approximated covariance matrix and a modified frequency domain vector of the received signal vector (S460). Finally, the components of the linear predictor are rearranged based on the linear predictor, a complex conjugate is obtained, and a predictor of the transmission signal vector is obtained using the inverse discrete Fourier transform (S470).

5 is a graph illustrating a calculation amount of a signal estimating apparatus according to an embodiment of the present invention. As shown in FIG. 5, as the observation period N increases, the length of the signal vector increases and thus the amount of computation increases.

Referring to FIG. 5, WLMMSE represents the amount of computation of the conventional linear estimation method, and the solid line of the present invention represents the computation amount of the signal estimation method proposed in the present invention. In the WLMMSE, the computational complexity of the conventional wide linear estimation method is proportional to the cube of N, and the signal estimation method according to the present invention is proportional to NlogN. Therefore, it can be confirmed that the signal estimation method proposed by the present invention has a much lower computational complexity.

6 is a graph illustrating a mean square error of a signal estimator according to an embodiment of the present invention.

Referring to FIG. 6, it has a mean square error that is much lower than that of the conventional linear predictor, and has a mean square error that is almost the same as that of the conventional wide linear predictor. As the observation period N increases, the average square error performance of the broad linear estimator is approached.

The embodiment according to the present invention may be applied to a processor such as a microprocessor, a controller, a microcontroller, an application specific integrated circuit (ASIC) or the like, or a component or a processor of the apparatus shown in Fig. 1, ≪ / RTI > 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.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the inventions as defined by the following claims It will be understood that various modifications and changes may be made thereto without departing from the spirit and scope of the invention.

Claims (20)

A method for estimating a transmission signal using a reception signal in a wireless communication system,
Sampling the received signal at regular intervals to generate a received signal vector;
Generating a frequency domain vector of the received signal vector using a discrete Fourier transform of a complex conjugate of the received signal vector and the received signal vector;
Generating a first modified frequency domain vector based on the frequency domain vector;
Approximating a covariance matrix of a second modified frequency domain vector associated with the transmitted signal vector based on a quadrature period normal signal characteristic; And
And estimating a transmitter signal vector estimator using the approximated covariance matrix and the first modified frequency domain vector.
The method according to claim 1,
Wherein the frequency domain vector is generated by modifying the complex conjugate of the received signal vector and the received signal vector into a frequency domain and then connecting the complex conjugate to the frequency domain.
The method according to claim 1,
The first modified frequency domain vector
Figure 112013029909827-pat00040
(
Figure 112013029909827-pat00041
Is a complex of a received signal vector, W MN z is a received signal a result of the discrete Fourier transform size MN for (z), M is the oversampling size, N is a symbol length, W MN z * is the received signal (z) And means for performing a discrete Fourier transform with a magnitude of MN for the conjugate.
The method according to claim 1,
Wherein the first transformed frequency domain vector is generated based on a multiplication of the received signal vector by a G matrix formed using a Kronecker product based on a zero matrix and an identity matrix. / RTI >
5. The method of claim 4,
The G matrix
Figure 112013029909827-pat00042
(Where G is a G matrix, I A is a unit matrix of size A × A, O A is a zero matrix of size A × A, M is the size of oversampling, N is a symbol length,
Figure 112013029909827-pat00043
Is a Kronecker product). ≪ / RTI >
5. The method of claim 4,
Wherein the first modified frequency domain vector is calculated through a method of eliminating an element at a corresponding position without being calculated by multiplying the received signal vector by the G matrix.
2. The method of claim 1, wherein the approximating step
The covariance matrix of the second modified frequency domain vector
Figure 112013029909827-pat00044
Where G is a discrete Fourier transform matrix,? Is a covariance matrix of the transmitted signal,? Is a covariance matrix of the second modified frequency domain vector,
Figure 112013029909827-pat00045
Is an inverse discrete Fourier transform matrix, I A Or 1 A is a unit matrix of size A × A, M is the size of the oversampling, N is the symbol length,
Figure 112013029909827-pat00046
(K) is a Kronecker product, and ⊙ is a Hadamard product).
3. The method of claim 1,
To estimate the second modified frequency domain vector,
Figure 112013029909827-pat00047
(here,
Figure 112013029909827-pat00048
The second linear predictor, Σ in the frequency domain vector is a second modified frequency domain approximation of the covariance matrix of the vector, σ 2 = N 0 M / T, N 0 is a particular value, M is the over sampling size, T is the time, I A is a unit matrix of size A × A, N is a symbol length,
Figure 112013029909827-pat00049
And a second transformed frequency domain vector). The method of claim 1,
9. The method according to claim 8,
Estimating a predictor of the transmitted signal vector using an inverse centered discrete Fourier transform after rearranging the components of the linear estimator of the second frequency domain vector and taking a portion of the complex conjugate; And estimating a transmission signal in the wireless communication system.
10. The method of claim 9,
Wherein the inverse discrete Fourier transform is an inverse operation of the conversion from the transmission signal vector to the second conversion frequency domain vector.
An apparatus for estimating a transmission signal using a reception signal in a wireless communication system,
A sampling unit for sampling the received signal at regular intervals to generate a received signal vector;
A frequency domain vector generator for generating a frequency domain vector of the received signal vector using the received signal vector and the discrete Fourier transform of the complex conjugate of the received signal vector;
A first transformed frequency domain vector generator for generating a first transformed frequency domain vector based on the frequency domain vector;
An approximation unit for approximating a covariance matrix of a second modified frequency domain vector associated with the transmitted signal vector; And
And estimating an estimator of a transmission signal vector using the approximated covariance matrix and the first modified frequency domain vector.
12. The method of claim 11,
Wherein the frequency domain vector is generated by transforming the complex conjugate of the received signal vector and the received signal vector into a frequency domain and then connecting the complex conjugate to the frequency domain.
12. The method of claim 11,
The first modified frequency domain vector
Figure 112013029909827-pat00050
(
Figure 112013029909827-pat00051
Is a complex of a received signal vector, W MN z is a received signal a result of the discrete Fourier transform size MN for (z), M is the oversampling size, N is a symbol length, W MN z * is the received signal (z) And a result of discrete Fourier transform having a magnitude of MN with respect to the conjugate is MN).
12. The method of claim 11,
Wherein the first transformed frequency domain vector is generated based on a multiplication of the received signal vector by a G matrix formed using a Kronecker product based on a zero matrix and an identity matrix. Of the transmission signal.
15. The method of claim 14,
The G matrix
Figure 112013029909827-pat00052
(Where G is a G matrix, I A is a unit matrix of size A × A, O A is a zero matrix of size A × A, M is the size of oversampling, N is a symbol length,
Figure 112013029909827-pat00053
Quot; means a Kronecker product). ≪ / RTI >
15. The method of claim 14,
Wherein the first modified frequency domain vector is calculated through a method of eliminating an element at a corresponding position without being calculated by multiplying the received signal vector by the G matrix.
12. The apparatus of claim 11, wherein the approximating unit
The covariance matrix of the second modified frequency domain vector
Figure 112013029909827-pat00054
Where G is a discrete Fourier transform matrix,? Is a covariance matrix of the transmitted signal,? Is a covariance matrix of the second modified frequency domain vector,
Figure 112013029909827-pat00055
Is an inverse discrete Fourier transform matrix, I A or 1 A is a unit matrix having a size of A × A, M is a size of oversampling, N is a symbol length,
Figure 112013029909827-pat00056
Is a Kronecker product, and ⊙ is a Hadamard product).
12. The apparatus of claim 11,
To estimate the second modified frequency domain vector,
Figure 112013029909827-pat00057
(here,
Figure 112013029909827-pat00058
The second linear predictor, Σ in the frequency domain vector is a second modified frequency domain approximation of the covariance matrix of the vector, σ 2 = N 0 M / T, N 0 is a particular value, M is the over sampling size, T is the time, I A is a unit matrix of size A × A, N is a symbol length,
Figure 112013029909827-pat00059
Is a vector of a first deformation frequency band) is used to estimate the transmission signal in the wireless communication system.
19. The apparatus of claim 18, wherein the estimating unit
And a predictor of the transmission signal vector is obtained using an inverse center discrete Fourier transform after rearranging the components of the linear predictor of the second frequency domain vector and taking a part of the complex conjugate. A transmission signal estimation apparatus in a wireless communication system.
20. The method of claim 19,
Wherein the inverse discrete Fourier transform is an inverse operation of the conversion from the transmission signal vector to the second conversion frequency domain vector.
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KR20020018328A (en) * 2000-09-01 2002-03-08 서평원 The Method for Cancelling Interference Signal in CDMA System with Antenna Array
KR20080093280A (en) * 2007-04-16 2008-10-21 한국전자통신연구원 Apparatus and method for receive diversity for detecting random access preambles in communictions system
KR20100056058A (en) * 2008-11-19 2010-05-27 포항공과대학교 산학협력단 Method and device of frequency domain equalization
KR20110128558A (en) * 2010-05-24 2011-11-30 포항공과대학교 산학협력단 Method and device of signal presence detection in the radio communication system based on cognitive radio

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20020018328A (en) * 2000-09-01 2002-03-08 서평원 The Method for Cancelling Interference Signal in CDMA System with Antenna Array
KR20080093280A (en) * 2007-04-16 2008-10-21 한국전자통신연구원 Apparatus and method for receive diversity for detecting random access preambles in communictions system
KR20100056058A (en) * 2008-11-19 2010-05-27 포항공과대학교 산학협력단 Method and device of frequency domain equalization
KR20110128558A (en) * 2010-05-24 2011-11-30 포항공과대학교 산학협력단 Method and device of signal presence detection in the radio communication system based on cognitive radio

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