Echo Cancellation of Voiceband Data Signals Using Recursive Least Squares and Stochastic Gradient Algorithms

Michael L. Honig*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The convergence properties of adaptive least squares (LS) and stochastic gradient (SG) algorithms are studied in the context of echo cancellation of voiceband data signals. The algorithms considered are the SG transversal, SG lattice, LS transversal (fast Kalman), and LS lattice. It is shown that for the channel estimation problem considered here, LS algorithms converge in approximately 2N iterations where N is the order of the filter. In contrast, both SG algorithms display inferior convergence properties due to their reliance upon statistical averages. Simulations are presented to verify this result, and indicate that the fast Kalman algorithm frequently displays numerical instability which can be circumvented by using the lattice structure. Finally, the equivalence between an LS algorithm and a fast converging modified SG algorithm which uses a maximum length input data sequence is shown.

Original languageEnglish (US)
Pages (from-to)65-73
Number of pages9
JournalIEEE Transactions on Communications
Volume33
Issue number1
DOIs
StatePublished - Jan 1985

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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