  This paper aims to initiate the research on dependence control, which
transforms the dependence structure of a stochastic process in the system
through dependence manipulation, to improve the system performance.
Specifically, we develop a dependence control theory for wireless channels,
focusing on three principles in dependence control: (i) the asymptotic decay
rates of delay and backlog in the system are the measures for dependence
comparison and ordering, (ii) the dependence in the arrival process and the
service process have a dual potency to influence the system performance, and
(iii) the manipulation of the dependence in the free dimensions of the arrival
or service process transforms the dependence structure of the arrival or
service process. In addition, we apply the theory to the Markov additive
process, which is a general model for a class of arrival processes and a
versatile model for wireless channel capacity, and derive a set of results for
various performance measures, including delay, backlog, and delay-constrained
capacity. To demonstrate the use of the theory, we focus on dependence
manipulation in wireless channel capacity, where we use copula to represent the
dependence structure of the underlying Markov process of wireless channel
capacity. We show that, based on a priori information of the temporal
dependence of the uncontrollable parameters and the spatial dependence between
the uncontrollable and controllable parameters, we can construct a sequence of
temporal copulas of the Markov process and obtain a sequence of transition
matrices of the controllable parameters to achieve the demanded dependence
properties of the wireless channel capacity. This dependence manipulation
technique is validated by simulation.
