Optimal Source Coding of Markov Chains for Real-Time Remote Estimation

TL;DR

This paper develops an optimal source coding policy for Markov chains where transmission and state updates occur simultaneously, reducing average transmission duration compared to benchmark policies through MDP formulation.

Contribution

It introduces a novel optimal coding policy for Markov chains under synchronized transmission and state update, outperforming Huffman-based benchmarks.

Findings

Optimal policy reduces average transmission duration.

Performance gain varies with Markov process parameters.

Benchmark policies are less efficient than the proposed method.

Abstract

We revisit the source coding problem for a Markov chain under the assumption that the transmission times and how fast the Markov chain transitions its state happen at the same time-scale. Specifically, we assume that the transmission of each bit takes a single time slot, and the Markov chain updates its state in the same time slot. Thus, the length of the codeword assigned to a symbol determines the number of non-transmitted symbols, as well as, the probability of the realization of the next symbol to be transmitted. We aim to minimize the average transmission duration over an infinite horizon by proposing an optimal source coding policy based on the last transmitted symbol and its transmission duration. To find the optimal policy, we formulate the problem with a Markov decision process (MDP) by augmenting the symbols alongside the transmission duration of the symbols. Finally, we analyze two Huffman-based benchmark policies and compare their performances with the proposed optimal policy. We observe that, in randomly generated processes, our proposed optimal policy decreases the average transmission duration compared to benchmark policies. The performance gain varies based on the parameters of the Markov process.