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Asymptotics of Input-Constrained Erasure Channel Capacity.
- Source :
-
IEEE Transactions on Information Theory . Jan2018, Vol. 64 Issue 1, p148-162. 15p. - Publication Year :
- 2018
-
Abstract
- In this paper, we examine an input-constrained erasure channel and we characterize the asymptotics of its capacity when the erasure rate is low. More specifically, for a general memoryless erasure channel with its input supported on an irreducible finite-type constraint, we derive partial asymptotics of its capacity, using some series expansion type formula of its mutual information rate; and for a binary erasure channel with its first-order Markovian input supported on the $(1, \infty )$ -RLL constraint based on the concavity of its mutual information rate with respect to some parameterization of the input, we numerically evaluate its first-order Markov capacity and further derive its full asymptotics. The asymptotics obtained in this paper, when compared with the recently derived feedback capacity for a binary erasure channel with the same input constraint, enable us to draw the conclusion that feedback may increase the capacity of an input-constrained channel, even if the channel is memoryless. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189448
- Volume :
- 64
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Information Theory
- Publication Type :
- Academic Journal
- Accession number :
- 126963944
- Full Text :
- https://doi.org/10.1109/TIT.2017.2742498