Abstract:
The invention relates to a method for channel estimation using a dedicated pilot signal in an OFDM-based communication system with multi-antenna transmission techniques. The receiver estimates the statistical characteristics of a virtual channel generated by beamforming by exploiting the statistical characteristics of common pilot channel, determines the optimum dedicated pilot pattern, and estimates the channel from dedicated pilot signal transmitted in an optimum pattern by means of AMMSE interpolation. The invention minimizes the channel estimation error by exploiting the statistical characteristics of the virtual channel, and optimally determines the dedicated pilot pattern according to the operation environment to maximize the transmission performance in terms of the pilot signaling overhead and the channel estimation error. In particular, the invention is very effective in low signal-to-interference plus noise power ratio (SINR) operation environments.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation-in-part of PCT International Application No. PCT/KR2010/008401, filed on Nov. 25, 2010, which claimed priority to Korean Application No. 10-2009-0114509, filed on Nov. 25, 2009, the entire disclosures of which are incorporated by reference as a part of this application. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a method and apparatus for channel estimation using a dedicated pilot signal in an orthogonal frequency division multiplexing (OFDM)-based wireless communication system using multi-antenna transmission techniques. 
     2. Description of the Related Art 
     An OFDM wireless communication system with multi-input multi-output (MIMO) techniques can increase the channel capacity using a spatial multiplexing technique and improve the reliability of data transmission using a diversity technique. Accurate channel estimation at the receiver is indispensable to maximize the performance of a MIMO OFDM-based wireless communication system. The channel information is often estimated using common pilot signal orthogonally allocated to each transmit antenna in the downlink of a MIMO OFDM-based wireless communication system. However, this technique may suffer from high pilot signaling overhead because the amount of common pilot signal increases in linear proportion to the number of transmit antennas. If the MIMO system uses the same amount of pilot signaling overhead as the single antenna system, it can use reduced pilot signal for each antenna, yielding poor channel estimation (i.e., increase of the mean square error (MSE) in channel estimation) in low signal-to-interference plus noise power ratio (SINR) channel environments. 
     In order to alleviate this problem, the fourth-generation (4G) wireless communication systems, such as IEEE 802.16m and 3GPP LTE, consider the use of a dedicated (or user specific) pilot signal. In an MIMO system which employs a transmit beamforming technique, the data signal is transmitted through a virtual channel which is generated by multiplying the channel with the beam weight vector for the transmit beamforming. The dedicated pilot signal is also transmitted by means of transmit beamforming with the same beam weight identical as that of data signal. The use of dedicated pilot signal is effective in low-SINR environments (e.g., near the cell boundary) mainly because of the reduction of pilot signaling overhead and the effect of beamforming gain as well. 
     The statistical characteristics of the virtual channel are different from those of individual antenna channel. Moreover, the amount of resource allocated to the dedicated pilot signal is very small. As a consequence, it may not be easy to estimate the statistical characteristics of the virtual channel from the received dedicated pilot signal. Conventional technologies often employs a linear interpolation (LI) technique because of the simplicity (i.e., it does not require the statistical characteristics of the virtual channel). However, the LI technique may be vulnerable to the presence of interference and noise (e.g., near the cell boundary). It also may not be able to minimize the pilot signaling overhead because it cannot optimally determine the pilot pattern according to the channel environment. 
     The use of a dedicated pilot signal considered in the 4G wireless communication systems is very effective in low-SINR environments mainly due to the improvement of transmit power gain from the beamforming and the reduction of the pilot signaling overhead. However, it may not be easy to estimate the statistical characteristics of the virtual channel because the dedicated pilot signal is allocated to a specific user using a small amount of resource. As a consequence, the use of statistical estimation techniques (e.g., the minimum mean squared estimation (MMSE)) may not be applicable to the channel estimation with the use of dedicated pilot signal, which may limit the exploitation of the dedicated pilot signal in the channel estimation. 
     SUMMARY OF THE INVENTION 
     In order to overcome the above-described limitations in the use of dedicated pilot signal, the present invention proposes a method and apparatus which can estimate the statistical characteristics of the virtual channel, generated by the transmit beamforming, using the statistical characteristics of the common pilot channel, determine the dedicated pilot pattern to maximize the system transmission rate, and estimate the virtual channel from the dedicated pilot signal. As a result, it can be possible to noticeably improve the channel estimation accuracy in low SINR environments by employing an optimum channel estimation technique with the use of statistical characteristics of the virtual channel, while improving the pilot transmit power and the reduction of pilot signaling overhead. 
     In order to accomplish the said object, the present invention providing a method and apparatus for the channel estimation using dedicated pilot signal in an OFDM-based wireless communication system with transmit beamforming includes the step and the unit for the estimation of the statistical characteristics of the virtual channel, which is generated by the transmit beamforming, using the statistical characteristics of the common pilot channel; the step and the unit for the determination of the dedicated pilot signal pattern using the estimated statistical characteristics of the virtual channel; and the step and the unit for the channel estimation using the dedicated pilot signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram showing the schematic configuration of a transmitter and a receiver which adaptively estimate the channel information using a dedicated pilot signal in a wireless communication system, according to an embodiment of the present invention; 
         FIG. 2  is a diagram showing a method of estimating the statistical characteristics of a virtual channel in a wireless communication system to which transmit beamforming techniques have been applied, according to an embodiment of the present invention; 
         FIG. 3  is a diagram showing the detailed configuration of the correlation function estimation unit included in the unit to estimate statistical characteristics of the virtual channel of  FIG. 1 ; 
         FIG. 4  is a diagram showing the method for the determination of dedicated pilot pattern in a wireless communication system with transmit beamforming, according to an embodiment of the present invention; 
         FIG. 5  is a diagram showing detailed configuration of the dedicated pilot pattern determination unit included in the unit for estimating statistical characteristics of the virtual channel of  FIG. 1 ; 
         FIG. 6  is a diagram showing the AMMSE interpolation process for the estimation of virtual channel in a wireless communication system with transmit beamforming according to an embodiment of the present invention; 
         FIG. 7  is a diagram showing the configuration of the AMMSE interpolation unit for the estimation of virtual channel as an example of the virtual channel interpolation unit  132  of  FIG. 1 ; and 
         FIG. 8  is a diagram showing the overall configuration of the channel estimator in a receiver of a wireless communication system with transmit beamforming, according to an embodiment of the present invention. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Preferred embodiments of the present invention will be described in what follows with reference to the accompanying drawings. In the following description, detailed descriptions of known functions and constructions which have been deemed to make the gist of the present invention vague will be omitted. 
     A method and apparatus according to embodiments of the present invention will be described below which, in an OFDM-based wireless communication system with multi-antenna techniques (e.g., coherent beamforming (CBF) or eigen-beamforming (EBF) technique), estimates the statistical characteristics of virtual channel generated by multi-antenna transmission techniques, using the statistical characteristics of common pilot channel; determines the dedicated pilot pattern maximizing the system transmission rate using the estimated statistical characteristics of the virtual channel, and estimates the virtual channel from the dedicated pilot pattern. 
     A MEMO OFDM-based wireless communication system using dedicated pilot signal with N T  transmit antennas will be considered. Let P d (n,k) be the dedicated pilot signal allocated to the n-th OFDM symbol and the k-th subcarrier in the wireless communication system. When the wireless communication system transmits the signal by means of beamforming with an (N T ×1)-unit norm beam weight vector, the received pilot signal can be represented by Equation 1:
 
 Y ( n,k )= H ( n,k ) w ( n,k ) P   d ( n,k )+ N ( n,k )  (1)
 
where H(n,k) is a (1×N T )-unit norm channel vector comprising zero-mean independently and identically distributed (i.i.d.) complex Gaussian random variables with the same variance σ H   2 , and N(n,k) is additive white Gaussian noise (AWGN) with variance σ N   2 . Here, each element of H(n,k) represents common pilot channel and the virtual channel due to the beamforming can be represented by the following Equation 2:
 
 H   w ( n,k )= H ( n,k ) w ( n,k )  (2)
 
     Let H i (n,k) be the i-th element of the channel vector H(n,k). Assuming that the channel is a wide sense stationary process, the correlation function of the common pilot channel can be defined by the following Equation 3:
 
 r   H (Δ n,Δk )= E{H   i ( n+Δn,k+Δk ) H*   i ( n,k )} for  i= 1,  . . . , N   i   (3)
 
where Δn and Δk are the OFDM symbol interval in the time domain and the subcarrier interval in the frequency domain, E{•} is the expectation operator, and the superscript * denotes the complex conjugate operator. The correlation function of the virtual channel can be defined by the following Equation 4:
 
 r   H     w   (Δ n,Δk )= E{H   w ( n+Δn,k+Δk ) H*   w ( n,k )}  (4)
 
       FIG. 1  is a diagram showing the schematic configuration of a transceiver which estimates the channel using dedicated pilot signal in a wireless communication system, according to an embodiment of the present invention. 
     The transmitter  101  includes a signal transmission unit  111  and a beamforming unit  112 . The transmission unit  111  transmits data and dedicated pilot signal having a unique pattern. The beamforming unit  112  generates transmit signal by applying the beam weight to the data and dedicated pilot signal, and transmits it through N T  transmit antennas. 
     The receiver  103  includes an estimation unit  113  and an interpolation unit  114 . The estimation unit  113  estimates the statistical characteristics of virtual channel generated by the beamforming unit  112 . The interpolation unit  132  estimates the virtual channel by interpolating the estimated virtual channel by means of affine MMSE based on the estimated statistical characteristics of the virtual channel. 
       FIG. 2  is a diagram showing the process for the estimation the statistical characteristics of the virtual channel generated by the transmit beamforming, according to an embodiment of the present invention. 
     At step  201 , the receiver receives common pilot signal and dedicated pilot signal as well. At step  203 , the receiver estimates the correlation function of the common pilot channel from the received common pilot signal, by Equation 3. At step  205 , the receiver estimates the correlation function of the virtual channel from the correlation function of the common pilot channel, estimated at step  203 . 
     The method for the estimation of the correlation function of the virtual channel depends on the transmit beamforming technique. If the CBF technique according to an embodiment of the present invention is used, the correlation function of the virtual channel can be estimated using the following Equation 5 
                       r     H   w       ⁡     (       Δ   ⁢           ⁢   n     ,     Δ   ⁢           ⁢   k       )       =         (       σ   H   2     →     σ   E   2       )     ⁢       (       Γ   ⁡     (       N   T     +     1   /   2       )         Γ   ⁡     (     N   T     )         )     2     ⁢       (     1   -          ρ        2     +           N   T   2     ⁡     (       Γ   ⁡     (     N   T     )         Γ   ⁡     (       N   T     +     1   /   2       )         )       4     ⁢        ρ            )       1   /   2         +       σ   E   2     ⁢     ρ   2                 (   5   )               
where Δn and Δk are respectively the OFDM symbol interval in the time domain and the subcarrier interval in the frequency domain, σ H   2  and σ E   2  are respectively the variance of the channel and the beam weight vector, N T  is the number of transmit antennas, Γ(·) is the gamma function, and ρ is the normalized correlation function of the common pilot channel, which is represented by the following Equation 6:
 
     
       
         
           
             
               
                 
                   ρ 
                   = 
                   
                     
                       
                         r 
                         H 
                       
                       ⁡ 
                       
                         ( 
                         
                           
                             Δ 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             n 
                           
                           , 
                           
                             Δ 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                         ) 
                       
                     
                     
                       σ 
                       H 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     If the EBF technique according to another embodiment of the present invention is used, the correlation function of the virtual channel can be estimated using the following Equation 7
 
 r   H     w   (Δ n,Δk )=ρλ max   (7)
 
where Δn and Δk are respectively the OFDM symbol interval in the time domain and the subcarrier interval in the frequency domain, ρ is the normalized correlation function of the common pilot channel, and λ max  is the maximum eigenvalue of the spatial correlation matrix of the channel, obtained by means of eigenvalue decomposition. The spatial correlation matrix is defined by the following Equation 8:
 
 R=E{H   H ( n,k ) H ( n,k )}  (8)
 
       FIG. 3  is a diagram showing the detailed configuration of the correlation function estimation unit included in the unit that estimates the statistical characteristics of the virtual channel of  FIG. 1 . 
     The correlation function estimation unit  300  includes a pilot signal reception unit  301 , a common pilot channel statistics estimation unit  303 , a transformation formula selection unit  305 , and a virtual channel statistics estimation unit  307 . 
     The pilot signal reception unit  301  receives common pilot signal and dedicated pilot signal transmitted from the transmitter. The common pilot channel statistics estimation unit  303  estimates the correlation function of the common pilot channel using the received common pilot signal. 
     The transformation formula selection unit  305  selects Equation 5 when the CBF technique is used, and Equation 7 when the EBF technique is used, and transfers it to the virtual channel statistics estimation unit  307 . 
     The virtual channel statistics estimation unit  307  estimates the correlation function of the virtual channel by applying the transformation formula determined by the transformation formula selection unit  305  to the correlation function of the common pilot channel estimated by the common pilot channel statistics estimation unit  303 . 
       FIG. 4  is a diagram showing a method of optimally determining the dedicated pilot pattern in a wireless communication system with transmit beamforming, according to an embodiment of the present invention. 
     At step  401 , an initial pattern is determined by the following Equation 9:
 
( d   t   ,d   f )=( d   t,0   ,d   f,0 )  (9)
 
where d t  is the OFDM symbol interval of the dedicated pilot in the time domain and d f  is the subcarrier interval of the dedicated pilot in the frequency domain.
 
     At step  403 , the average MSE with the use of dedicated pilot pattern (d t +Δd t ,d f ) is compared with that with the use of dedicated pilot pattern (d t ,d f +Δd f ) using the correlation function of the virtual channel estimated at step  205  of  FIG. 2 , and then the dedicated pilot pattern yielding a smaller MSE is selected, where Δd t  and Δd f  are a fixed unit for the spacing of pilot signal in the time and the frequency domain, respectively. In general, the initial pattern (d t,0 ,d f,0 ), and the spacing intervals Δd t  and Δd f  can be set to 1, corresponding to the minimum interval of the OFDM symbol and the subcarrier. 
     At step  403 , the increase of the spacing interval (i.e., the reduction of the pilot signal density) of the pilot signal in the time or frequency domain should be selected so as to less increase in the MSE due to the reduction of the pilot signal density. In particular, repeated processing of step  403  may converge to a dedicated pilot pattern that minimizes the average MSE with respect to the density of the pilot signal. 
     At step  403 , when the receiver estimates the virtual channel using an interpolation filter with N t  taps in the time domain and N f  taps in the frequency domain, it calculates the average MSE for the channel estimation in the resource region defined by the following Equation 10:
 
 S ={( n″,k ″)| n′≦n″≦n ′+( N   t −1) d   t   ,k′≦k″≦k ′+( N   f −1) d   f }  (10)
 
where n′ and k′ are the location of the OFDM symbol and subcarrier of the received dedicated pilot signal in the time and frequency domain, respectively, n″ and k″ are the location of the OFDM symbol and subcarrier to be estimated in the time and frequency domain, respectively, and (d t ,d f ) is the pattern of the dedicated pilot signal transmitted from the transmitter.
 
     The average MSE for the channel estimation is calculated in the resource region S defined by Equation 10 by the following Equation 11: 
                         σ   _     MSE   2     ⁡     (       d   t     ,     d   f       )       =       ∑       n   ″     =     n   ′           n   ′     +       (       N   t     -   1     )     ⁢     d   t           ⁢           ⁢       ∑       k   ″     =     k   ′           k   ′     +       (       N   f     -   1     )     ⁢     d   f           ⁢           ⁢       σ   MSE   2     ⁡     (       n   ″     ,     k   ″     ,     d   t     ,     d   f       )                   (   11   )               
where σ MSE   2 (n″,k″,d t ,d f ) denotes the MSE when the channel at the n″-th OFDM symbol and the k″-th subcarrier is estimated by means of AMMSE interpolation, represented as the following Equation 12:
 
σ MSE   2 ( n″,k″,d   t   ,d   f )=σ H     w     2 −θ T ( n″,k″,d   t   ,d   f )Φ −1 ( d   t   ,d   f )θ*( n″,k″,d   t   ,d   f ).  (12)
 
Here σ H     w     2  is the variance of the virtual channel. The variance of the virtual channel with the use of CBF and EBF can respectively be calculated by the following Equations 13 and 14:
 
                     σ       H   w     ;   CBF     2     =         (       σ   H   2     -     σ   E   2       )     ⁢     (       N   T     -       (       Γ   ⁡     (       N   T     +     1   /   2       )         Γ   ⁡     (     N   T     )         )     2       )       +     σ   E   2               (   13   )                 σ       H   w     ;   EBF     2     =     λ   max             (   14   )               
θ(n″,k″,d t ,d f ) is the cross-covariance vector defined by the following Equation 15 and Φ(d t ,d f ) is the auto-covariance matrix defined by the following Equation 16:
 
θ( n″,k″,d   t   ,d   f )= E{Ĥ   tap ( d   t   ,d   f )·( H   w ( n″,k ″)− m   w )*}  (15)
 
Φ( d   t   ,d   f )= E{Ĥ   tap ( d   t   ,d   f )· Ĥ   tap   H ( d   t   ,d   f )}  (16)
 
where Ĥ tap (d t ,d f ) is an (N t ·N f ×1)-unit norm vector representing the measurements of the virtual channel in the resource region S, whose i-th element [Ĥ tap (d t ,d f )] t  of Ĥ tap (d t ,d f ) is defined by the following Equation 17:
 
                       [         H   ^     tap     ⁡     (       d   t     ,     d   f       )       ]     i     =         Y   ⁡     (         n   ′     +       d   t     ·     (     i   -       N   t     ·     (       ⌈     i     N   t       ⌉     -   1     )         )         ,       k   ′     +       d   f     ·     (       ⌈     i     N   t       ⌉     -   1     )           )           P   d     ⁡     (         n   ′     +       d   t     ·     (     i   -       N   t     ·     (       ⌈     i     N   t       ⌉     -   1     )         )         ,       k   ′     +       d   f     ·     (       ⌈     i     N   t       ⌉     -   1     )           )         -       m   w     .               (   17   )               
Here i=1, . . . , N t ·N, ┌N┐ is the least integer larger than or equal to N and m w  is the average channel gain of the virtual channel. The average channel gain of the virtual channel with the use of CBF and EBF can respectively be calculated by the following Equation 18 and 19:
 
     
       
         
           
             
               
                 
                   
                     m 
                     
                       
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                         w 
                       
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                         w 
                       
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                   19 
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     Comparing the average MSE with the use of two pilot patterns (d t +Δd t ,d f ) and (d t ,d f +Δd f ), the new pilot pattern (d t,new ,d f,new ) is selected by the following Equation 20: 
     
       
         
           
             
               
                 
                   
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     At step  405 , the system transmission rate with the use of dedicated pilot pattern (d t ,d f ) and (d t,new ,d f,new ) selected at step  403  is compared by the following Equation 21. If the system transmission rate is increased with the use of the new pilot pattern, returns to step  403  while replacing the dedicated pilot pattern with the new dedicated pilot pattern and otherwise, proceeds to step  407  without change of the dedicated pilot pattern:
 
 i )( d   t   ,d   f )←( d   t,new   ,d   f,new ) if  T ( d   t,new   ,d   f,new )&gt; T ( d   t   ,d   f )
 
 ii )( d   t,opt   ,d   f,opt )←( d   t   ,d   f ) if  T ( d   t,new   ,d   f,new )≦ T ( d   t   ,d   f )  (21)
 
where T(d t ,d f ) is the system transmission rate taking into account of the signaling overhead for the dedicated pilot signaling and the MSE, given by Equation 22:
 
                     T   ⁡     (       d   t     ,     d   f       )       =       (     1   -     1       d   t     ⁢     d   f           )     ·         log   2     (     1   +         (       m     H   w     2     +     σ     H   w     2       )     -         σ   _     MSE   2     ⁡     (       d   t     ,     d   f       )                 σ   _     MSE   2     ⁡     (       d   t     ,     d   f       )       +     1   /   γ           )     .               (   22   )               
Here γ is the average SNR value.
 
     At step  407 , the optimum dedicated pilot pattern (d t,opt ,d f,opt ) is transmitted to the transmitter, which is determined by repeatedly processing steps  403  and  405 . 
       FIG. 5  is a diagram showing the detailed configuration of the dedicated pilot pattern determination unit included in the unit that estimates the statistical characteristics of the virtual channel of  FIG. 1 . 
     The dedicated pilot pattern determination unit  500  includes an initial pattern determination unit  501 , an MSE comparison unit  503 , a system transmission rate comparison unit  505 , and a final pattern feedback unit  507 . 
     The initial pattern determination unit  501  determines the initial dedicated pilot pattern by Equation 9. The MSE comparison unit  503  selects a pilot pattern yielding a smaller MSE with the use of two pilot patterns; one whose pilot spacing is increased by one unit in the time domain and the other one whose pilot spacing is increased by one unit in the frequency domain, according to Equation 20. 
     The system transmission rate comparison unit  505  compares the system transmission rate with the use of the existing pilot pattern and new pilot pattern determined by Equation 21. If the system transmission rate is increased, it replaces the pilot pattern with the new pilot pattern and transfers it to the MSE comparison unit  503 , and repeats the previous process. If the system transmission rate is not increased, it uses the existing pilot pattern as the optimum one and transfers it to the final pattern feedback unit  507 . The final pattern feedback unit  507  transfers the optimum dedicated pilot pattern to the transmitter. 
       FIG. 6  is a diagram showing a method of AMMSE interpolation for the estimation of the virtual channel in a wireless communication system with transmit beamforming, according to an embodiment of the present invention. 
     At step  601 , the virtual channel is estimated from the received dedicated pilot signal by means of the least square (LS) method, according to Equation 17. 
     At step  603 , the cross-covariance vector and the auto-covariance matrix are calculated using the statistical characteristics of the virtual channel estimated at step  205  of  FIG. 2  and the optimum dedicated pilot pattern determined at step  407  of  FIG. 4 . 
     At step  603 , when a dedicated pilot signal is transmitted at the n′-th OFDM symbol and the k′-th subcarrier using dedicated pilot pattern (d t,opt ,d f,opt ) determined at step  407  of  FIG. 4 , the cross-covariance vector and the auto-covariance matrix for the estimation of the virtual channel in resource region S defined by Equation 10 can be calculated by the following Equations 23 and 24:
 
θ( n″,k″,d   t,opt   ,d   f,opt )= E{Ĥ   tap ( d   t,opt   ,d   f,opt )·( H   w ( n″,k ″)− m   w )*}  (23)
 
Φ( d   t,opt   ,d   f,opt )= E{Ĥ   tap ( d   t,opt   ,d   f,opt )· Ĥ   tap   H ( d   t,opt   ,d   f,opt )}  (24)
 
     At step  605 , from the cross-covariance vector and the auto-covariance matrix calculated by Equations 23 and 24, the tap coefficient of the AMMSE interpolation filter minimizing the MSE is determined by the following Equation 25:
 
 W=θ   T ( n″,k″,d   t,opt   ,d   f,opt )Φ −1 ( d   t,opt   ,d   f,opt )  (25)
 
     At step  607 , the virtual channel in resource region s is estimated by means of AMMES interpolation with the tap coefficient determined at step  605 , by Equation 26:
 
 Ĥ   w ( n″,k ″)= WĤ   tap ( d   t,opt   ,d   f,opt )+ m   H     w     (26)
 
       FIG. 7  is a diagram showing the detailed configuration of the AMMSE interpolation unit as an example of the virtual channel interpolation unit  132  of  FIG. 1 . 
     The virtual channel AMMSE interpolation unit  700  includes an LS estimation unit  701 , a covariance calculation unit  703 , an AMMSE interpolation filter coefficient calculation unit  705 , and an AMMSE interpolation unit  707 . The received dedicated pilot signal is used to estimate the virtual channel by means of the LS method at the LS estimation unit  701 , by Equation 17: 
     The covariance calculation unit  703  calculates the cross-covariance vector and auto-covariance matrix used for the AMMSE interpolation by Equations 23 and 24, using the statistical characteristics of the virtual channel estimated at step  205  and the optimum dedicated pilot pattern determined at step  407 . 
     The AMMSE interpolation filter coefficient calculation unit  705  determines the coefficient of the AMMSE interpolation filter using the results of the covariance calculation unit  703 , by Equation 25. The AMMSE interpolation unit  707  interpolates the virtual channel using the coefficient of the AMMSE interpolation filter determined by the AMMSE interpolation filter coefficient calculation unit  705 , by Equation 26. 
       FIG. 8  is a diagram showing the overall configuration of the receiver in a wireless communication system with transmit beamforming, according to an embodiment of the present invention. 
     The receiver includes the unit for the estimation of statistical characteristics of the virtual channel, including the correlation function estimation unit  300  of  FIG. 3  and the optimum dedicated pilot pattern determination unit  500  of  FIG. 5 , and the AMMSE interpolation unit  700  of  FIG. 7 . 
     Common pilot signal received from the pilot signal reception unit  301  is used by the common pilot channel statistics estimation unit  303  to estimate the correlation function of the common pilot channel. 
     The transformation formula selection unit  305  selects the transformation formula (for example, Equation 5 or 7) according to the beamforming technique, and transfers it to the virtual channel statistics estimation unit  307 . 
     The virtual channel statistics estimation unit  307  estimates the correlation function of the virtual channel by applying the transferred transformation formula to the correlation function of the common pilot channel estimated by the common pilot channel statistics estimation unit  303 . 
     The initial pattern determination unit  501  determines an initial pattern for the dedicated pilot by Equation 9. The MSE comparison unit  503  changes the initial pilot pattern so that the MSE is minimized with respect to the pilot signal density, using the correlation function of the virtual channel estimated by the virtual channel statistics estimation unit  307 , by Equation 20. 
     The system transmission rate comparison unit  505  determines the pilot pattern by Equation 21 so that the system transmission rate is maximized with respect to the pilot signaling overhead in association with the MSE comparison unit  503 . The final pattern feedback unit  507  reports the final dedicated pilot pattern obtained in the above process to the transmitter. The transmitter transmits dedicated pilot signal using the dedicated pilot pattern reported from unit  507 . The LS estimation unit  701  in the receiver estimates the virtual channel value as in Equation 17 by means of the LS method from the received dedicated pilot signal. 
     The covariance calculation unit  703  calculates the cross-covariance vector and auto-covariance matrix using the correlation function of the virtual channel estimated by the virtual channel statistics estimation unit  307  and the optimum dedicated pilot pattern determined by the final pattern feedback unit  507 , as in Equations 23 and 24, respectively. 
     The AMMSE interpolation filter coefficient calculation unit  705  calculates the tap coefficient of an AMMSE interpolation filter by Equation 25 using the results of the covariance calculation unit  703 . The AMMSE interpolation unit  707  estimates the virtual channel generated by the beamforming by applying the virtual channel estimated by the LS estimation unit  701  to the AMMSE interpolation filter, as in Equation 26. 
     The present invention, in a wireless communication system using a multi-antenna transmit beamforming technique, can estimate the statistical characteristics of the virtual channel generated by the transmit beamforming, using the statistical characteristics of the common pilot channel; determines the optimum dedicated pilot pattern using the estimated statistical characteristics of the virtual channel; and estimates the channel information by interpolating the channel information estimated from the dedicated pilot signal by means of affine minimum mean squared error (AMMSE) interpolation. In particular, the present invention presents a scheme and method that can practically utilize the dedicated pilot signal in a wireless communication system with transmit beamforming, by estimating the correlation function of the virtual channel from the estimated correlation function of the common pilot channel, and applying to the channel estimation with the use of an AMMSE interpolation technique. The present invention can optimally determine the dedicated pilot pattern according to the system transmission rate, maximizing the channel estimation performance in terms of the signaling overhead of dedicated pilot signal and the channel estimation MSE as well.