References of "Sidorenko, Vladimir"
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See detailComputation of Moments in the Trellis
Heim, Axel; Sidorenko, Vladimir; Sorger, Ulrich UL

in Proc. IEEE International Symposium on Information Theory (2008)

Decisions on sources with memory transmitted over independent channels can be taken by employing trellis calculations. In this paper, it is shown that for a certain class of functions their moments can be ... [more ▼]

Decisions on sources with memory transmitted over independent channels can be taken by employing trellis calculations. In this paper, it is shown that for a certain class of functions their moments can be computed in the trellis, too. This is done by generalizing the forward/backward recursion known from the BCJR algorithm [1]. In analogy to the symbol probabilities, by introducing a constraint at a certain depth in the trellis we obtain symbol moments. These moments are required for an ef?cient implementation of the discriminated belief propagation algorithm in [2], and can furthermore be utilized to compute conditional entropies in the trellis. The moment computation algorithm has the same asymptotic complexity as the BCJR algorithm. It is applicable to any commutative semi-ring, thus also providing a generalization of the Viterbi algorithm [3]. [less ▲]

Detailed reference viewed: 122 (2 UL)
Peer Reviewed
See detailComputation of distributions and their moments in the trellis
Heim, Axel; Sidorenko, Vladimir; Sorger, Ulrich UL

in Advances in Mathematics of Communications (2008), 2(4), 373391

Consider a function whose set of vector arguments with known distribution is described by a trellis. For a certain class of functions, the distribution of the function values can be calculated in the ... [more ▼]

Consider a function whose set of vector arguments with known distribution is described by a trellis. For a certain class of functions, the distribution of the function values can be calculated in the trellis. The forward/backward recursion known from the BCJR algorithm [2] is generalized to compute the moments of these distributions. In analogy to the symbol probabilities, by introducing a constraint at a certain depth in the trellis we obtain symbol distributions and symbol moments, respectively. These moments are required for an efficient implementation of the discriminated belief propagation algorithm in [8], and can furthermore be utilized to compute conditional entropies in the trellis. The moment computation algorithm has the same asymptotic complexity as the BCJR algorithm. It is applicable to any commutative semi-ring, thus actually providing a generalization of the Viterbi algorithm [10]. [less ▲]

Detailed reference viewed: 121 (3 UL)