Deriving the Probabilistic Capacity of General Run-Length Sets Using Generating Functions

TL;DR

This paper proves that for general run-length sets, the probabilistic capacity equals the combinatorial capacity under Shannon's original definition, using generating functions for a unified information-theoretic approach.

Contribution

It extends the equivalence of probabilistic and combinatorial capacity to general run-length sets under Shannon's definition, using generating functions.

Findings

Probabilistic capacity equals combinatorial capacity for general run-length sets.

The derivation uses generating functions for constrained systems.

Provides a unified information-theoretic framework.

Abstract

In "Reliable Communication in the Absence of a Common Clock" (Yeung et al., 2009), the authors introduce general run-length sets, which form a class of constrained systems that permit run-lengths from a countably infinite set. For a particular definition of probabilistic capacity, they show that probabilistic capacity is equal to combinatorial capacity. In the present work, it is shown that the same result also holds for Shannon's original definition of probabilistic capacity. The derivation presented here is based on generating functions of constrained systems as developed in "On the Capacity of Constrained Systems" (Boecherer et al., 2010) and provides a unified information-theoretic treatment of general run-length sets.