Adaptive error constrained LMS algorithms and its blind equalization method

Sooyong Choi, Kyunbyoung Ko, Daesik Hong

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)


In this paper, the adaptive error constrained least mean square (AECLMS) algorithm is proposed through the extension and generalization of the noise constrained LMS (NCLMS) algorithm and its performance analysis is presented. By using a constrained optimization technique, the assumption that the noise variance is known is eliminated. Therefore, the proposed constrained optimization method can be easily applied to blind equalization methods. The proposed constrained method is also applied to constant modulus criterion. The proposed method can accelerates the convergence speed of the conventional steepest descent-type training procedure by several times.

Original languageEnglish
Number of pages5
Publication statusPublished - 2001
EventIEEE Global Telecommunications Conference GLOBECOM'01 - San Antonio, TX, United States
Duration: 2001 Nov 252001 Nov 29


OtherIEEE Global Telecommunications Conference GLOBECOM'01
Country/TerritoryUnited States
CitySan Antonio, TX

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Global and Planetary Change


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