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Feedback Capacity of Finite-State Channels with Causal State Known at the Encoder

Shemuel, Eli and Sabag, Oron and Permuter, Haim (2020) Feedback Capacity of Finite-State Channels with Causal State Known at the Encoder. In: 2020 IEEE International Symposium on Information Theory (ISIT). IEEE , Piscataway, NJ, pp. 2120-2125. ISBN 9781728164328. https://resolver.caltech.edu/CaltechAUTHORS:20200831-151813925

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Abstract

We consider finite state channels (FSCs) with feedback and state known causally at the encoder. This setting is general and includes both a channel with a Markovian state in which the state is input-independent, but also many other cases where the state is input-dependent such as the energy harvesting model. We characterize the capacity as a multi-letter expression that includes auxiliary random variables with memory. We derive a single-letter computable lower bound based on auxiliary directed graphs that are used to provide an auxiliary structure for the channel outputs and are called Q-graphs. This method is implemented for binary energy-harvesting model with a unitsized battery and the noiseless channel, whose exact capacity has remained an open problem. We identify a structure of Q-graphs, with achievable rates that outperform the best achievable rates known in the literature.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/isit44484.2020.9174446DOIArticle
ORCID:
AuthorORCID
Sabag, Oron0000-0002-7907-1463
Permuter, Haim0000-0003-3170-3190
Additional Information:© 2020 IEEE.
Record Number:CaltechAUTHORS:20200831-151813925
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200831-151813925
Official Citation:E. Shemuel, O. Sabag and H. Permuter, "Feedback Capacity of Finite-State Channels with Causal State Known at the Encoder," 2020 IEEE International Symposium on Information Theory (ISIT), Los Angeles, CA, USA, 2020, pp. 2120-2125, doi: 10.1109/ISIT44484.2020.9174446
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105184
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Sep 2020 23:58
Last Modified:08 Sep 2020 23:58

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