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Matrix representations of trellises and enumerating trellis pseudocodewords

Authors: David Conti; Nigel Boston;

Matrix representations of trellises and enumerating trellis pseudocodewords

Abstract

Tail-biting trellises and their pseudocodewords are very important for modern decoding techniques like iterative decoding. We introduce a useful matrix representation of trellises, give its fundamental properties, and use it to enumerate and describe the distribution of trellis pseudocodewords. We give several examples, a couple of which lead to important open problems. Next, we prove that the pseudocodeword weight enumerator introduced in [2] always satisfies a recurrence equation, and, for certain trellises including the Golay trellis given in [3], that it is invariant under generalized MacWilliams transformations, allowing invariant theory to be used for computing it. Computation for the Golay trellis shows then that pseudocodewords of period at most 4 must have AWGN pseudoweight at least 8.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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