Monotonic optimization based decoding for linear codes

Publication Type:
Journal Article
Citation:
Journal of Global Optimization, 2013, 55 (2), pp. 301 - 312
Issue Date:
2013-01-01
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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. © 2011 Springer Science+Business Media, LLC.
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