Multivariate Analytic Combinatorics for Cost Constrained Channels and Subsequence Enumeration (2111.06105v2)

Abstract: Analytic combinatorics in several variables is a powerful tool for deriving the asymptotic behavior of combinatorial quantities by analyzing multivariate generating functions. We study information-theoretic questions about sequences in a discrete noiseless channel under cost and forbidden substring constraints. Our main contributions involve the relationship between the graph structure of the channel and the singularities of the bivariate generating function whose coefficients are the number of sequences satisfying the constraints. We combine these new results with methods from multivariate analytic combinatorics to solve questions in many application areas. For example, we determine the optimal coded synthesis rate for DNA data storage when the synthesis supersequence is any periodic string. This follows from a precise characterization of the number of subsequences of an arbitrary periodic strings. Along the way, we provide a new proof of the equivalence of the combinatorial and probabilistic definitions of the cost-constrained capacity, and we show that the cost-constrained channel capacity is determined by a cost-dependent singularity, generalizing Shannon's classical result for unconstrained capacity.