A Deterministic Algorithm for the Capacity of Finite-State Channels

We propose two modified versions of the classical gradient ascent method to compute the capacity of finite-state channels with Markovian inputs. For the case that the channel mutual information rate is strongly concave in a parameter taking values in a compact convex subset of some Euclidean space,...

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Vydané v:	IEEE transactions on information theory Ročník 68; číslo 3; s. 1465 - 1479
Hlavní autori:	Wu, Chengyu, Han, Guangyue, Anantharam, Venkat, Marcus, Brian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Channel capacity Channels Convergence Euclidean geometry Euclidean space finite-state channels gradient ascent hidden Markov processes Markov processes Monte Carlo methods Mutual information Polynomials Power capacitors
ISSN:	0018-9448, 1557-9654
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