Iterative Decoding using Eigenmessages

Moon, Todd K.; Crockett, J.S.; Gunther, Jacob H.; Chauhan, Ojas

doi:10.1109/tcomm.2009.12.050230

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…The notation ∼xi denotes the "summary" operation [1], summing out all variables except x i . Following [11], denote the message from a function node F i to an adjacent variable node V i (in the clockwise direction of figure 1) as…”

Section: Messagesmentioning

confidence: 99%

“…Other efforts have included attempts to compensate for higher density parity check matrices using stochastic decoding [4,5,6,7,8] or high-complexity local adjustments to the parity check matrix [9,10]. Compensation for the effect of cycles to speed convergence has also been examined in [11]. An exploration of decoding on a graph with a single cycle appears in [12].…”

Section: Introductionmentioning

confidence: 99%

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Compensation methods for cycles in message passing decoders

Moon

Gunther

2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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It is well established that cycles in the Tanner graphs associated with error correction codes introduces biases into message passing decoding and result in poor performance as the number of cycles in the graph increases. The decoder in this paper addresses the problem of cycles by two methods:(1) Computing exact probabilities by marginalizing the exact probability obtained using clique potential functions and Clifford-Hammersley theorem, followed by pushing these probabilities as messages on adjacent to the graph; and (2) finding a set of random embedded subgraphs as minimal spanning trees, then passing messages along these. Modest probability of error improvements are obtained on very short codes.

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Section: Messagesmentioning

confidence: 99%