Sparseness-controlled adaptive tap algorithms for partial update adaptive filters

Jie Yang, Xiaoming Zhu, Gerald E. Sobelman, Keshab K. Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we present a novel adaptive tap algorithm for partial update adaptive filters used in network echo cancellation. As the channel is typically long and sparse, it is unnecessary and inefficient to update all of the taps. Although partial update algorithms can be used to solve this problem, it is difficult to predetermine a fixed number of partial-update taps without a priori knowledge of the channel. We propose a partial update strategy which adapts not only the filter coefficients but also the number of taps to be updated. A novel adaptive tap partial update algorithm, Sparseness-Controlled Adaptive Tap IPNLMS-MMAX (SC-AT IPNLMS-MMAX), is proposed which incorporates a new measure for sparseness. Simulation results show that, compared with the fully updated IPNLMS algorithm, our proposed partial update algorithm has both faster initial convergence and lower computational complexity while achieving almost the same steady-state error.

Original languageEnglish (US)
Title of host publicationICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing
DOIs
StatePublished - Dec 1 2009
Event7th International Conference on Information, Communications and Signal Processing, ICICS 2009 - Macau Fisherman's Wharf, Macao
Duration: Dec 8 2009Dec 10 2009

Publication series

NameICICS 2009 - Conference Proceedings of the 7th International Conference on Information, Communications and Signal Processing

Other

Other7th International Conference on Information, Communications and Signal Processing, ICICS 2009
CountryMacao
CityMacau Fisherman's Wharf
Period12/8/0912/10/09

Keywords

  • Adaptive filter
  • Adaptive tap
  • IPNLMS
  • MMAX
  • PNLMS
  • Partial update
  • Proportionate adaptation
  • Sparse impulse response

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