Analysis and design of tuned turbo codes

Christian Koller, Alexandre Graell I Amat, Jörg Kliewer, Francesca Vatta, Kamil Sh Zigangirov, Daniel J. Costello

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


It has been widely observed that there exists a fundamental tradeoff between the minimum (Hamming) distance properties and the iterative decoding convergence behavior of turbo-like codes. While capacity-achieving code ensembles typically are asymptotically bad in the sense that their minimum distance does not grow linearly with block length, and they therefore exhibit an error floor at moderate-to-high signal-to-noise ratios, asymptotically good codes usually converge further away from channel capacity. In this paper, we introduce the concept of tuned turbo codes, a family of asymptotically good hybrid concatenated code ensembles, where asymptotic minimum distance growth rates, convergence thresholds, and code rates can be tradedoff using two tuning parameters: λ and μ. By decreasing λ, the asymptotic minimum distance growth rate is reduced in exchange for improved iterative decoding convergence behavior, while increasing λ raises the asymptotic minimum distance growth rate at the expense of worse convergence behavior, and thus, the code performance can be tuned to fit the desired application. By decreasing μ, a similar tuning behavior can be achieved for higher rate code ensembles.

Original languageEnglish (US)
Article number6200859
Pages (from-to)4796-4813
Number of pages18
JournalIEEE Transactions on Information Theory
Issue number7
StatePublished - 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Concatenated codes
  • Hamming distance
  • distance growth rates
  • extrinsic information transfer (EXIT) charts
  • iterative decoding
  • turbo codes


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