Joint Empirical Coordination of Source and Channel
Ma\"el Le Treust

TL;DR
This paper explores how autonomous communication devices can coordinate their actions in a decentralized network by achieving specific joint symbol distributions, optimizing a shared utility function.
Contribution
It characterizes the set of achievable joint distributions in a point-to-point source-channel model with decentralized policies and maximizes a common utility function.
Findings
Achievable distributions are characterized for non-causal encoder and strictly causal decoder.
The framework generalizes compression and transmission as special cases of empirical coordination.
Optimal policies can be designed to maximize shared utility in decentralized networks.
Abstract
In a decentralized and self-configuring network, the communication devices are considered as autonomous decision-makers that sense their environment and that implement optimal transmission schemes. It is essential that these autonomous devices cooperate and coordinate their actions, to ensure the reliability of the transmissions and the stability of the network. We study a point-to-point scenario in which the encoder and the decoder implement decentralized policies that are coordinated. The coordination is measured in terms of empirical frequency of symbols of source and channel. The encoder and the decoder perform a coding scheme such that the empirical distribution of the symbols is close to a target joint probability distribution. We characterize the set of achievable target probability distributions for a point-to-point source-channel model, in which the encoder is non-causal and…
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Taxonomy
TopicsGame Theory and Applications · Diffusion and Search Dynamics · Opinion Dynamics and Social Influence
