Cascade multiterminal source coding
Paul Cuff (Stanford University), Han-I Su (Stanford University), Abbas, El Gamal (Stanford University)

TL;DR
This paper studies the limits of distributed source coding in a cascade network for correlated sources, providing bounds on rates for lossy and lossless reconstruction, with specific insights for Gaussian sources.
Contribution
It introduces inner and outer bounds on the rate region for cascade multiterminal source coding with arbitrary distortion functions, including a threshold analysis for Gaussian sources.
Findings
Inner and outer bounds on achievable rate regions are established.
A threshold is identified for optimal encoding strategies in Gaussian cases.
Relaying can outperform recompression depending on source variances.
Abstract
We investigate distributed source coding of two correlated sources X and Y where messages are passed to a decoder in a cascade fashion. The encoder of X sends a message at rate R_1 to the encoder of Y. The encoder of Y then sends a message to the decoder at rate R_2 based both on Y and on the message it received about X. The decoder's task is to estimate a function of X and Y. For example, we consider the minimum mean squared-error distortion when encoding the sum of jointly Gaussian random variables under these constraints. We also characterize the rates needed to reconstruct a function of X and Y losslessly. Our general contribution toward understanding the limits of the cascade multiterminal source coding network is in the form of inner and outer bounds on the achievable rate region for satisfying a distortion constraint for an arbitrary distortion function d(x,y,z). The inner…
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Taxonomy
TopicsWireless Communication Security Techniques · DNA and Biological Computing · Chaos-based Image/Signal Encryption
