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Monotonic optimization based decoding for linear codes

Author

Listed:
  • H. Tuan
  • T. Son
  • H. Tuy
  • P. Khoa

Abstract

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. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • H. Tuan & T. Son & H. Tuy & P. Khoa, 2013. "Monotonic optimization based decoding for linear codes," Journal of Global Optimization, Springer, vol. 55(2), pages 301-312, February.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:2:p:301-312
    DOI: 10.1007/s10898-011-9816-9
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    Cited by:

    1. Pham Thi Hoai & Hoai An Le Thi & Nguyen Canh Nam, 2021. "Half-open polyblock for the representation of the search region in multiobjective optimization problems: its application and computational aspects," 4OR, Springer, vol. 19(1), pages 41-70, March.

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