Divergence Scaling for Distribution Matching

TL;DR

This paper demonstrates that the informational divergence of distribution matchers for finite alphabets increases logarithmically with block length, extending a fundamental binary string result to more general cases.

Contribution

It generalizes the known divergence growth behavior from binary strings to finite alphabet distribution matchers.

Findings

Divergence grows logarithmically with block length.

Generalization from binary to finite alphabets.

Provides theoretical insight into distribution matching behavior.

Abstract

Distribution matchers for finite alphabets are shown to have informational divergences that grow logarithmically with the block length, generalizing a basic result for binary strings.