The ORD-based least squares lattice algorithm: Some computer simulations using finite wordlengths

The QR-decomposition (QSD)-based least-squares lattice algorithm and its architecture are described. This algorithm can be used to solve least-squares minimization problems that involve time-series data. The results of some computer simulation experiments on an adaptive channel equalizer using the QRD-based lattice algorithm are presented. These simulations were performed using limited-precision floating-point arithmetic. The results show that very little penalty is paid in reducing the computational load. The QRD-based lattice algorithm works essentially as well as the QRD-based triangular systolic array but requires only O(p/sup 2/N) operations per time instant as compared with O(p/sup 2/N/sup 2/) for the array. The results also confirm that a square-root-free form of the algorithm is empirically better than the standard form.<<ETX>>

[1]  J. G. McWhirter,et al.  Recursive Least-Squares Minimization Using A Systolic Array , 1983, Optics & Photonics.

[2]  Fuyun Ling,et al.  A recursive modified Gram-Schmidt algorithm for least- squares estimation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[3]  John M. Cioffi,et al.  The fast adaptive ROTOR's RLS algorithm , 1990, IEEE Trans. Acoust. Speech Signal Process..

[4]  John M. Cioffi,et al.  High-speed systolic implementation of fast QR adaptive filters , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[5]  P. S. Lewis QR algorithm and array architectures for multichannel adaptive least squares lattice filters , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[6]  F. Ling Efficient least-squares lattice algorithms based on Givens rotation with systolic array implementations , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[7]  B. Yang,et al.  Parallel implementations of adaptive multichannel least squares lattice filters , 1990, IEEE International Symposium on Circuits and Systems.

[8]  M. Bellanger Adaptive filter theory: by Simon Haykin, McMaster University, Hamilton, Ontario L8S 4LB, Canada, in: Prentice-Hall Information and System Sciences Series, published by Prentice-Hall, Englewood Cliffs, NJ 07632, U.S.A., 1986, xvii+590 pp., ISBN 0-13-004052-5 025 , 1987 .

[9]  J. G. McWhirter,et al.  A novel algorithm and architecture for adaptive digital beamforming , 1986 .

[10]  Fuyun Ling Givens rotation based least squares lattice and related algorithms , 1991, IEEE Trans. Signal Process..

[11]  I.K. Proudler,et al.  Computationally efficient QRD-based wide-band beamforming , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[12]  Paul S. Lewis,et al.  QR-based algorithms for multichannel adaptive least squares lattice filters , 1990, IEEE Trans. Acoust. Speech Signal Process..

[13]  Phillip A. Regalia,et al.  On the duality between fast QR methods and lattice methods in least squares adaptive filtering , 1991, IEEE Trans. Signal Process..