Non-homogeneous random walks on a semi-infinite strip

Abstract

We study the asymptotic behaviour of Markov chains $(X_n,{\eta }_n)$ on ${\mathbb{Z}}_{+}\times S$, where ${\mathbb{Z}}_{+}$ is the non-negative integers and $S$ is a finite set. Neither coordinate is assumed to be Markov. We assume a moments bound on the jumps of $X_n$, and that, roughly speaking, ${\eta }_n$ is close to being Markov when $X_n$ is large. This departure from much of the literature, which assumes that ${\eta }_n$ is itself a Markov chain, enables us to probe precisely the recurrence phase transitions by assuming asymptotically zero drift for $X_n$ given ${\eta }_n$. We give a recurrence classification in terms of increment moment parameters for $X_n$ and the stationary distribution for the large-$X$ limit of ${\eta }_n$. In the null case we also provide a weak convergence result, which demonstrates a form of asymptotic independence between $X_n$ (rescaled) and ${\eta }_n$. Our results can be seen as generalizations of Lamperti's results for non-homogeneous random walks on ${\mathbb{Z}}_{+}$ (the case where $S$ is a singleton). Motivation arises from modulated queues or processes with hidden variables where ${\eta }_n$ tracks an internal state of the system.

27 pages

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