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It is well known that opportunistic scheduling algorithms are throughputoptimal under dynamic channel and network conditions. However, these algorithmsachieve a hypothetical rate region which does not take into account theoverhead associated with channel probing and feedback required to obtain thefull channel state information at every slot. In this work, we design a jointscheduling and channel probing algorithm by considering the overhead ofobtaining the channel state information. We adopt a correlated andnon-stationary channel model, which is more realistic than those used in theliterature. We use concepts from learning and information theory to accuratelytrack channel variations to minimize the number of channels probed at everyslot, while scheduling users to maximize the achievable rate region of thenetwork. More specifically, we employ Gaussian Process Regression that enableus to predict future channel state and also provide a way to measureuncertainty in the learning and prediction. Simulation results show that withthe proposed algorithm, the network can carry higher user traffic. The proposedsolution can be utilized in more complex and heterogeneous environments such as5G and beyond networks where the model free approach is needed.

Smart Scheduling and Feedback Allocation over Non-stationary Wireless Channels