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We consider a utility maximization problem over partially observable MarkovON/OFF channels. In this network instantaneous channel states are never known,and at most one user is selected for service in every slot according to thepartial channel information provided by past observations. Solving the utilitymaximization problem directly is difficult because it involves solvingpartially observable Markov decision processes. Instead, we construct anapproximate solution by optimizing the network utility only over a goodconstrained network capacity region rendered by stationary policies. Using anovel frame-based Lyapunov drift argument, we design a policy of admissioncontrol and user selection that stabilizes the network with utility that can bemade arbitrarily close to the optimal in the constrained region. Equivalently,we are dealing with a high-dimensional restless bandit problem with a generalfunctional objective over Markov ON/OFF restless bandits. Thus the networkcontrol algorithm developed in this paper serves as a new approximationmethodology to attack such complex restless bandit problems.

Network Utility Maximization over Partially Observable Markovian Channels