TY - JOUR
T1 - Channel coding and source coding with increased partial side information
AU - Sadeh-Shirazi, Avihay
AU - Basher, Uria
AU - Permuter, Haim
N1 - Funding Information: Acknowledgments: Avihay Sadeh-Shirazi, Uria Basher and Haim Permuter were supported in part by the Israel Science Foundation and in part by the European Research Council under the EuropeanUnion’s Seventh Framework Programme (FP7/2007-2013)/ERC under Grant 337752. Publisher Copyright: © 2017 by the authors.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - Let (S1,i, S2,i) ~ i.i.d p(s1, s2), i = 1, 2, ... be a memoryless, correlated partial side information sequence. In this work, we study channel coding and source coding problems where the partial side information (S1, S2) is available at the encoder and the decoder, respectively, and, additionally, either the encoder's or the decoder's side information is increased by a limited-rate description of the other's partial side information. We derive six special cases of channel coding and source coding problems and we characterize the capacity and the rate-distortion functions for the different cases. We present a duality between the channel capacity and the rate-distortion cases we study. In order to find numerical solutions for our channel capacity and rate-distortion problems, we use the Blahut-Arimoto algorithm and convex optimization tools. Finally, we provide several examples corresponding to the channel capacity and the rate-distortion cases we presented.
AB - Let (S1,i, S2,i) ~ i.i.d p(s1, s2), i = 1, 2, ... be a memoryless, correlated partial side information sequence. In this work, we study channel coding and source coding problems where the partial side information (S1, S2) is available at the encoder and the decoder, respectively, and, additionally, either the encoder's or the decoder's side information is increased by a limited-rate description of the other's partial side information. We derive six special cases of channel coding and source coding problems and we characterize the capacity and the rate-distortion functions for the different cases. We present a duality between the channel capacity and the rate-distortion cases we study. In order to find numerical solutions for our channel capacity and rate-distortion problems, we use the Blahut-Arimoto algorithm and convex optimization tools. Finally, we provide several examples corresponding to the channel capacity and the rate-distortion cases we presented.
KW - Blahut-Arimoto algorithm
KW - Channel capacity
KW - Channel coding
KW - Duality
KW - Gelfand-Pinsker channel coding
KW - Partial side information
KW - Rate-distortion
KW - Source coding
KW - Wyner-Ziv source coding
UR - http://www.scopus.com/inward/record.url?scp=85029143124&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/e19090467
DO - https://doi.org/10.3390/e19090467
M3 - Article
SN - 1099-4300
VL - 19
JO - Entropy
JF - Entropy
IS - 9
M1 - 467
ER -