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


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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    • "Splitting row-modules by partitioning check node operations has been shown to provide substantial gains in the required area and power efficiency [22] [23]. In another prominent line of work, researchers have proposed various stochastic decoding algorithms [10] [35] [33] [34] [24] [19] [20] [31]. They are all based on stochastic representation of the SP messages. "
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