2 research outputs found
Stochastic Interpretation for the Arimoto Algorithm
The Arimoto algorithm computes the Gallager function for a given channel and parameter
, by means of alternating maximization. Along the way, it generates a
sequence of input distributions , , ... , that
converges to the maximizing input . We propose a stochastic
interpretation for the Arimoto algorithm. We show that for a random (i.i.d.)
codebook with a distribution , the next distribution
in the Arimoto algorithm is equal to the type () of the
feasible transmitted codeword that maximizes the conditional Gallager exponent
(conditioned on a specific transmitted codeword type ). This
interpretation is a first step toward finding a stochastic mechanism for
on-line channel input adaptation.Comment: 5 pages, 1 figure, accepted for 2015 IEEE Information Theory
Workshop, Jerusalem, Israe