  This paper introduces a new theoretical framework for optimizing second-order
behaviors of wireless networks. Unlike existing techniques for network utility
maximization, which only considers first-order statistics, this framework
models every random process by its mean and temporal variance. The inclusion of
temporal variance makes this framework well-suited for modeling stateful fading
wireless channels and emerging network performance metrics such as
age-of-information (AoI). Using this framework, we sharply characterize the
second-order capacity region of wireless access networks. We also propose a
simple scheduling policy and prove that it can achieve every interior point in
the second-order capacity region. To demonstrate the utility of this framework,
we apply it for an important open problem: the optimization of AoI over
Gilbert-Elliott channels. We show that this framework provides a very accurate
characterization of AoI. Moreover, it leads to a tractable scheduling policy
that outperforms other existing work.
