Linear prediction based multipath channel identification algorithm

In digital communication systems, the pulse-shaping filter in the transmitter and the anti-aliasing filter in the receiver are often known. In this brief, this a priori knowledge is exploited to simplify the channel identification problem. The multipath identification problem is formulated as a homogeneous linear equation, in which a matrix is formed from the optimal linear predictor. Instead of solving directly the underlying linear equation, the estimate is obtained by finding the minor eigenvector of a matrix. The presented analysis shows that the performance of the proposed approach degrades significantly when the order is over estimated. An effective approach is then proposed to remove the redundant components in the estimated multipath channel vector. The obtained results show that the proposed approach is able to enhance the performance of the linear programming approach significantly when the order is over estimated.

[1]  Philippe Loubaton,et al.  Prediction error method for second-order blind identification , 1997, IEEE Trans. Signal Process..

[2]  Zhi Ding,et al.  Multipath channel identification based on partial system information , 1997, IEEE Trans. Signal Process..

[3]  Jean Pierre Delmas,et al.  On the robustness of the linear prediction method for blind channel identification with respect to effective channel undermodeling/overmodeling , 2000, IEEE Trans. Signal Process..

[4]  Constantinos B. Papadias,et al.  Further results on blind identification and equalization of multiple FIR channels , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[5]  Constantinos B. Papadias,et al.  Fractionally spaced equalization of linear polyphase channels and related blind techniques based on multichannel linear prediction , 1999, IEEE Trans. Signal Process..

[6]  H. Howard Fan,et al.  Linear prediction methods for blind fractionally spaced equalization , 2000, IEEE Trans. Signal Process..

[7]  Lang Tong,et al.  Blind identification and equalization based on second-order statistics: a time domain approach , 1994, IEEE Trans. Inf. Theory.

[8]  Dirk T. M. Slock,et al.  Blind channel estimation exploiting transmission filter knowledge , 2000, Signal Process..

[9]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[10]  Dirk T. M. Slock,et al.  Blind fractionally-spaced equalization, perfect-reconstruction filter banks and multichannel linear prediction , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Eric Moulines,et al.  Blind knowledge based algorithms based on second order statistics , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[12]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1995, IEEE Trans. Signal Process..

[13]  Jean Pierre Delmas,et al.  Blind channel approximation: effective channel order determination , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).