QR-decomposition decision feedback equalisation and finite-precision results

The QRD LS algorithm is generally recognised for its superior numerical properties under finite-precision implementation. Furthermore, its systolic architecture is well suited for VLSI implementation. In DFE applications the inherent and implementation nonlinearities make it impossible to analyse precisely all effects of finite-precision arithmetic. The paper presents finite precision results of a QRD LS DFE using the TMS320C25 16-bit precision DSP processors as a simulation platform. Using the bit error rate as a performance measure, results are presented for 2-PSK, 4-PSK and Pi/4-DQPSK modulation formats. Also presented are the numerical accuracy and convergence sensitivity for the filter weights. These results may serve as a basis for practical implementation of QRD LS DFEs.

[1]  Robert W. Stewart,et al.  Fast stable Kalman filter algorithms utilising the square root , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[2]  I. Proudler,et al.  Computationally efficient QR decomposition approach to least squares adaptive filtering , 1991 .

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

[4]  H. T. Kung.  Why systolic architectures? , 1982, Computer.

[5]  Shigeo Tsujii,et al.  Effect of floating-point error reduction with recursive least square for parallel architecture , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[6]  S. Kung,et al.  VLSI Array processors , 1985, IEEE ASSP Magazine.

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

[8]  John M. Cioffi,et al.  Limited-precision effects in adaptive filtering , 1987 .

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

[10]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

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

[12]  Joos Vandewalle,et al.  Recursive least squares with stabilized inverse factorization , 1990, Signal Process..

[13]  S. F. Hsieh,et al.  Systolic implementation of windowed recursive LS estimation , 1990, IEEE International Symposium on Circuits and Systems.

[14]  Paul S. Lewis.  Multichannel recursive least squares adaptive filtering without a desired signal , 1991, IEEE Trans. Signal Process..

[15]  Kui Liu.  Dynamic range of finite-precision recursive QRD LS algorithm and its stability , 1990, IEEE International Symposium on Circuits and Systems.

[16]  J. G. McWhirter,et al.  The ORD-based least squares lattice algorithm: Some computer simulations using finite wordlengths , 1990, IEEE International Symposium on Circuits and Systems.

[17]  Henry Leung,et al.  Stability of recursive QRD-LS algorithms using finite-precision systolic array implementation , 1989, IEEE Trans. Acoust. Speech Signal Process..

[18]  John G. Proakis,et al.  Introduction to Digital Signal Processing , 1988 .

[19]  Sun-Yuan Kung,et al.  On supercomputing with systolic/wavefront array processors , 1984 .

[20]  S. T. Alexander,et al.  Stable recursive least squares filtering using an inverse QR decomposition , 1990, International Conference on Acoustics, Speech, and Signal Processing.