Monotonic optimization based decoding for linear codes

H. D. Tuan, T. T. Son, H. Tuy, P. T. Khoa

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3 Citations (Scopus)


New efficient methods are developed for the optimal maximum-likelihood (ML) decoding of an arbitrary binary linear code based on data received from any discrete Gaussian channel. The decoding algorithm is based on monotonic optimization that is minimizing a difference of monotonic (d.m.) objective functions subject to the 0-1 constraints of bit variables. The iterative process converges to the global optimal ML solution after finitely many steps. The proposed algorithm's computational complexity depends on input sequence length k which is much less than the codeword length n, especially for a codes with small code rate. The viability of the developed is verified through simulations on different coding schemes.

Original languageEnglish
Pages (from-to)301-312
Number of pages12
JournalJournal of Global Optimization
Issue number2
Publication statusPublished - 2013
Externally publishedYes


  • Global optimization
  • Linear codes
  • Low density parity check (LDPC) codes
  • Maximum likelihood decoding

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