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.

翻译：我们通过经验协调框架研究马尔可夫信道上的联合信源信道编码。具体而言，我们旨在确定编码方案所能诱发的信源与信道符号的经验分布。我们考虑仅生成信道输入但不掌握过去信道状态的严格因果编码器，从而驱动马尔可夫状态的演化。我们的主要结果是网络中所有符号可协调的联合分布集的单字母内界与外界。为建立内界，我们引入了一种新的典型性概念——输入驱动马尔可夫典型性，并发展了其基本性质。与经典依赖离散无记忆信道块独立性的块马尔可夫编码方案不同，我们的分析直接利用马尔可夫信道结构，并超越了基于独立性的论证方法。