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 current 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 blockwise independence for discrete memoryless channels, our analysis directly exploits the Markov channel structure and improves beyond the independence-based arguments.

翻译：本研究通过经验协调框架探讨马尔可夫信道上的联合信源信道编码问题。具体而言，我们旨在确定可通过编码方案诱导的信源与信道符号的经验分布。我们考虑严格因果编码器，该编码器在无法获取过往信道状态的情况下生成信道输入，从而驱动当前马尔可夫状态的演化。我们的主要成果是建立了可实现联合分布集合的单字母内界与外界，该集合协调网络中的所有符号。为构建内界，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。与离散无记忆信道中依赖块间独立性的经典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。