Performance of proportionate-type NLMS algorithm with gain allocation proportional to the mean square weight deviation

The complex colored water-filling algorithm for gain allocation has been shown to provide improved mean square error convergence performance, relative to standard complex proportionate-type normalized least mean square algorithms. This algorithm requires sorting operations and matrix multiplication on the order of the size of the impulse response at each iteration. In this paper, the mean square weight deviation and two suboptimal gain allocation algorithms are presented. They are motivated by similar algorithms introduced before for real-valued signals and systems. The presented algorithms no longer require sorting. It is shown that they provide significant computational complexity savings while maintaining comparable mean square error convergence performance. The algorithms are also investigated in the case of unknown input correlation matrix and speech input signals.

[1]  Kevin T. Wagner,et al.  Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input , 2011, IEEE Transactions on Signal Processing.

[2]  Patrick A. Naylor,et al.  A Class of Sparseness-Controlled Algorithms for Echo Cancellation , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  Ali H. Sayed,et al.  Mean-square performance of a convex combination of two adaptive filters , 2006, IEEE Transactions on Signal Processing.

[4]  R. Farebrother.  Linear least squares computations , 1988 .

[5]  Kevin T. Wagner,et al.  Comparisons of one and two adaptation gain complex PtNLMS algorithms , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[6]  M. Doroslovački,et al.  On proportionate-type NLMS algorithms for fast decay of output error at all times , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[7]  Kevin T. Wagner,et al.  Complex colored water-filling algorithm for gain allocation in proportionate adaptive filtering , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[8]  Kevin T. Wagner,et al.  Complex proportionate-type normalized least mean square algorithms , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  I. Fijalkow,et al.  Characterization of an empirically-derived database of time-varying microwave channel responses , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[10]  Kevin T. Wagner,et al.  Reduced computational complexity suboptimal gain allocation for proportionate-type NLMS algorithms with colored inputs , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[11]  Kevin T. Wagner,et al.  Proportionate-type normalized least mean square algorithm with gain allocation motivated by minimization of mean-square-weight deviation for colored input , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Kevin T. Wagner,et al.  Gain allocation in proportionate-type NLMS algorithms for fast decay of output error at all times , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.