1 research outputs found
Analysis of Sequential Decoding Complexity Using the Berry-Esseen Inequality
his study presents a novel technique to estimate the computational complexity
of sequential decoding using the Berry-Esseen theorem. Unlike the theoretical
bounds determined by the conventional central limit theorem argument, which
often holds only for sufficiently large codeword length, the new bound obtained
from the Berry-Esseen theorem is valid for any blocklength. The accuracy of the
new bound is then examined for two sequential decoding algorithms, an
ordering-free variant of the generalized Dijkstra's algorithm (GDA)(or
simplified GDA) and the maximum-likelihood sequential decoding algorithm
(MLSDA). Empirically investigating codes of small blocklength reveals that the
theoretical upper bound for the simplified GDA almost matches the simulation
results as the signal-to-noise ratio (SNR) per information bit () is
greater than or equal to 8 dB. However, the theoretical bound may become
markedly higher than the simulated average complexity when is small.
For the MLSDA, the theoretical upper bound is quite close to the simulation
results for both high SNR ( dB) and low SNR (
dB). Even for moderate SNR, the simulation results and the theoretical bound
differ by at most \makeblue{0.8} on a scale.Comment: Submitted to the IEEE Trans. on Information Theory, 30 pages, 9
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