Constrained coding upper bounds via Goulden-Jackson cluster theorem

TL;DR

This paper introduces a combinatorial approach using the Goulden-Jackson cluster theorem to compute exact cardinalities and capacities of constrained codes, advancing beyond traditional spectral methods especially for finite and infinite constraint sets.

Contribution

It applies the Goulden-Jackson cluster theorem to constrained coding, providing a systematic way to compute exact code sizes and capacities, and links it to spectral methods.

Findings

Exact formulas for code cardinalities derived

Connection established between spectral radius and generating function

Upper bounds provided for variable-length non-overlapping codes

Abstract

Motivated by applications in DNA-based data storage, constrained codes have attracted a considerable amount of attention from both academia and industry. We study the maximum cardinality of constrained codes for which the constraints can be characterized by a set of forbidden substrings, where by a substring we mean some consecutive coordinates in a string. For finite-type constrained codes (for which the set of forbidden substrings is finite), one can compute their capacity (code rate) by the ``spectral method'', i.e., by applying the Perron-Frobenious theorem to the de Brujin graph defined by the code. However, there was no systematic method to compute the exact cardinality of these codes. We show that there is a surprisingly powerful method arising from enumerative combinatorics, which is based on the Goulden-Jackson cluster theorem (previously not known to the coding community), that can be used to compute not only the capacity, but also the exact formula for the cardinality of these codes, for each fixed code length. Moreover, this can be done by solving a system of linear equations of size equal to the number of constraints. We also show that the spectral method and the cluster method are inherently related by establishing a direct connection between the spectral radius of the de Brujin graph used in the first method and the convergence radius of the generating function used in the second method. Lastly, to demonstrate the flexibility of the new method, we use it to give an explicit upper bound on the maximum cardinality of variable-length non-overlapping codes, which are a class of constrained codes defined by an infinite number of forbidden substrings.