  It is well known that for ergodic channel processes the Generalized
Max-Weight Matching (GMWM) scheduling policy stabilizes the network for any
supportable arrival rate vector within the network capacity region. This
policy, however, often requires the solution of an NP-hard optimization
problem. This has motivated many researchers to develop sub-optimal algorithms
that approximate the GMWM policy in selecting schedule vectors. One implicit
assumption commonly shared in this context is that during the algorithm
runtime, the channel states remain effectively unchanged. This assumption may
not hold as the time needed to select near-optimal schedule vectors usually
increases quickly with the network size. In this paper, we incorporate channel
variations and the time-efficiency of sub-optimal algorithms into the scheduler
design, to dynamically tune the algorithm runtime considering the tradeoff
between algorithm efficiency and its robustness to changing channel states.
Specifically, we propose a Dynamic Control Policy (DCP) that operates on top of
a given sub-optimal algorithm, and dynamically but in a large time-scale
adjusts the time given to the algorithm according to queue backlog and channel
correlations. This policy does not require knowledge of the structure of the
given sub-optimal algorithm, and with low overhead can be implemented in a
distributed manner. Using a novel Lyapunov analysis, we characterize the
throughput stability region induced by DCP and show that our characterization
can be tight. We also show that the throughput stability region of DCP is at
least as large as that of any other static policy. Finally, we provide two case
studies to gain further intuition into the performance of DCP.
