Empirical Coordination over Markov Channel with Independent Source
Abstract
We study joint source-channel coding over Markov channels through the empirical coordination framework. More specifically, we aim at determining the empirical distributions of source and channel symbols that can be induced by a coding scheme. We consider strictly causal encoders that generate channel inputs, without access to the past channel states, henceforth driving the Markov state evolution. Our main result is the single-letter inner and outer bounds of the set of achievable joint distributions, coordinating all the symbols in the network. To establish the inner bound, we introduce a new notion of typicality, the input-driven Markov typicality, and develop its fundamental properties. Contrary to the classical block-Markov coding schemes that rely on the blockwise independence for discrete memoryless channels, our analysis directly exploits the Markov channel structure and improves beyond the independence-based arguments.
Cite
@article{arxiv.2601.11520,
title = {Empirical Coordination over Markov Channel with Independent Source},
author = {Mengyuan Zhao and Maël Le Treust and Tobias J. Oechtering},
journal= {arXiv preprint arXiv:2601.11520},
year = {2026}
}