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Fast Adaptive Algorithms For Signal Separation, Robert A. Manzo
Fast Adaptive Algorithms For Signal Separation, Robert A. Manzo
Theses
LMS and RLS type algorithms are suggested for decorrelation of multi-channel systems outputs. These algorithms act as signal separators when applied to unknown linear combinations of the inputs. The performance of the suggested algorithms is compared with that of the conventional LMS and RLS algorithms that minimize the mean square error. It. is shown that the correlation matrix eigenvalue spread associated with the LMS decorrelator is always smaller than the eigenvalue spread corresponding to the conventional LMS. resulting in faster convergence speed for the decorrelator. A new RLS type decorrelator algorithm is suggested. The RLS decorrelator is shown to be …
On Issues Of Equalization With The Decorrelation Algorithm : Fast Converging Structures And Finite-Precision, Andrew James Bateman
On Issues Of Equalization With The Decorrelation Algorithm : Fast Converging Structures And Finite-Precision, Andrew James Bateman
Theses
To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and …