  This paper proposes a dynamic primal-dual type algorithm to solve the optimal
scheduling problem in wireless networks subject to uncertain parameters, which
are generated by stochastic network processes such as random packet arrivals,
channel fading, and node mobilities. The algorithm is a generalization of the
well-known max-weight scheduling algorithm proposed by Tassiulas et al., where
only queue length information is used for computing the schedules when the
arrival rates are uncertain. Using the technique of fluid limits, sample path
convergence of the algorithm to an arbitrarily close to optimal solution is
proved, under the assumption that the Strong Law of Large Numbers (SLLN)
applies to the random processes which generate the uncertain parameters. The
performance of the algorithm is further verified by simulation results. The
method may potentially be applied to other applications where dynamic
algorithms for convex problems with uncertain parameters are needed.
