Computer Science – Information Theory
Scientific paper
2010-04-15
Computer Science
Information Theory
revised version. with new figures, simplified notation and more proof details
Scientific paper
We consider the source-channel separation architecture for lossy source coding in general communication networks. It is shown that the separation approach is optimal in two general scenarios, and is approximately optimal in a third scenario. The two general scenarios for which separation is optimal complement each other: the first scenario is when the memoryless sources at source nodes are arbitrarily correlated, each of which is to be reconstructed at possibly multiple destinations within certain distortions, but the channels in this network are synchronized, orthogonal and memoryless point-to-point channels; the second scenario is when the memoryless sources are mutually independent, each of which is to be reconstructed only at one destination within a certain distortion, but the channels are general, including multi-user channels such as multiple access, broadcast, interference and relay channels, possibly with feedback. The third general scenario, for which we demonstrate approximate optimality of source-channel separation, relaxes the second scenario by allowing each source to be reconstructed at multiple destinations. For this case, the loss from optimality by using the separation approach can be upper-bounded when the ``difference" distortion measure is taken, and in the special case of quadratic distortion measure, this leads to universal constant bounds. These results are shown without explicitly characterizing the achievable joint source-channel coding distortion region or the achievable separation-based coding distortion region. Furthermore, for the first general scenario, the extracted pure network source-coding problem has to incorporate a large number of rounds of user interactions and the corresponding causality constraints, which suggests a distinct research direction into interactive network source coding that has not received much attention in the literature.
Chen Jun
Diggavi Suhas
Shamai Shlomo
Tian Chao
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