Interior point least squares estimation: Transient convergence analysis and application to MMSE decision-feedback equalization

Kaywan H. Afkhamie, Zhi Quan Luo, Kon Max Wong

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In many communication systems, training sequences are used to help the receiver identify and/or equalize the channel. The amount of training data required depends on the convergence properties of the adaptive filtering algorithms used for equalization. In this paper, we propose the use of a new adaptive filtering method called interior point least squares (IPLS) for adaptive equalization. First, we show that IPLS converges exponentially fast in the transient phase. Then, we use the IPLS algorithm to update the weight vector for a minimum-mean-square-error decision-feedback equalizer (MMSE-DFE) in a CDMA down-link scenario. Numerical simulations show that when training sequences are short, IPLS consistently outperforms RLS in terms of system bit-error-rate and packet error rate. As the training sequence gets longer IPLS matches the performance of the RLS algorithm.

Original languageEnglish (US)
Pages (from-to)1543-1555
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume49
Issue number7
DOIs
StatePublished - Jul 2001

Bibliographical note

Funding Information:
Manuscript received November 8, 1999; revised March 12, 2001. This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant OPG0090391. The associate editor coordinating the review of this paper and approving it for publication was Prof. Dimitrios Hatzinakos.

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

  • Decision feedback equalization
  • Interior point methods
  • Least-squares algorithms
  • Transient analysis

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