Low complexity turbo-equalization: A clustering approach

Kyeongyeon Kim, Jun Won Choi, Suleyman S. Kozat, Andrew C. Singer

Research output: Contribution to journalArticlepeer-review


We introduce a low complexity approach to iterative equalization and decoding, or 'turbo equalization', which uses clustered models to better match the nonlinear relationship that exists between likelihood information from a channel decoder and the symbol estimates that arise in soft-input channel equalization. The introduced clustered turbo equalizer uses piecewise linear models to capture the nonlinear dependency of the linear minimum mean square error (MMSE) symbol estimate on the symbol likelihoods produced by the channel decoder and maintains a computational complexity that is only linear in the channel memory. By partitioning the space of likelihood information from the decoder based on either hard or soft clustering and using locally-linear adaptive equalizers within each clustered region, the performance gap between the linear MMSE turbo equalizers and low-complexity least mean square (LMS)-based linear turbo equalizers can be narrowed.

Original languageEnglish (US)
Article number6784321
Pages (from-to)1063-1066
Number of pages4
JournalIEEE Communications Letters
Issue number6
StatePublished - Jun 2014


  • Turbo equalization
  • hard clustering
  • piecewise linear modeling
  • soft clustering

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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