Conference Paper

A Markov chain model for Edge Memories in stochastic decoding of LDPC codes

Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
DOI: 10.1109/CISS.2011.5766114 Conference: Information Sciences and Systems (CISS), 2011 45th Annual Conference on
Source: IEEE Xplore

ABSTRACT Stochastic decoding is a recently proposed method for decoding Low-Density Parity-Check (LDPC) codes. Stochastic decoding is, however, sensitive to the switching activity of stochastic bits, which can result in a latching problem. Using Edge Memories (EMs) has been proposed as a method to counter the latching problem in stochastic decoding. In this paper, we introduce a Markov chain model for EMs and study state transitions over decoding cycles. The proposed method can be used to determine the convergence and the required number of decoding cycles in stochastic decoding. Moreover, it can help to study the behavior of decoding process and to estimate the decoding time.

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    ABSTRACT: A modified Gradient Descent Bit Flipping (GDBF) algorithm is proposed for decoding Low Density Parity Check (LDPC) codes. The algorithm, called Noisy GDBF (NGDBF) offers improvement in terms of performance by adding a random perturbation at each iteration to escape from undesirable local maxima. Both single-bit and multi-bit flipping versions of the algorithm are proposed and evaluated. The proposed single-bit and multi-bit versions of the algorithm are shown to improve the Bit Error Rate (BER) compared to previous GDBF algorithms of comparable complexity. The multi-bit NGDBF algorithm achieves a 0.5dB coding gain compared to the best GDBF algorithms previously reported. Unlike other multi-bit GDBF algorithms that provide an escape from local maxima, the proposed algorithm does not require computing a global objective function or a sort over all symbol metrics, making it highly efficient in comparison. Architectural details are presented for implementing the NGDBF algorithm. Complexity analysis and optimizations are also discussed.
    IEEE Transactions on Communications 02/2014; · 1.98 Impact Factor

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