8 research outputs found
An Efficient Joint Source-Channel Decoder with Dynamical Block Priors
An efficient joint source-channel (s/c) decoder based on the side information
of the source and on the MN-Gallager algorithm over Galois fields is presented.
The dynamical block priors (DBP) are derived either from a statistical
mechanical approach via calculation of the entropy for the correlated
sequences, or from the Markovian transition matrix. The Markovian joint s/c
decoder has many advantages over the statistical mechanical approach. In
particular, there is no need for the construction and the diagonalization of a
qXq matrix and for a solution to saddle point equations in q dimensions. Using
parametric estimation, an efficient joint s/c decoder with the lack of side
information is discussed. Besides the variant joint s/c decoders presented, we
also show that the available sets of autocorrelations consist of a convex
volume, and its structure can be found using the Simplex algorithm.Comment: 13 pages, to appear in "Progress in Theoretical Physics Supplement",
May 200
Parallel versus sequential updating for Belief Propagation decoding
sequential updating scheme (SUS) for the belief propagation algorithm is
proposed, and is compared with the parallel (regular) updating scheme (PUS).
Simulation results on various codes indicate that the number of iterations of
the belief algorithm for the SUS is about one half of the required iterations
for the PUS, where both decoding algorithms have the same error correction
properties. The complexity per iteration for both schemes is similar, resulting
in a lower total complexity for the SUS. The explanation of this effect is
related to the inter-iteration information sharing, which is a property of only
the SUS, and which increases the "correction gain" per iterationComment: 15 pages, 3 figures, submitted to Phys. Rev. E june 200