  It is well known that opportunistic scheduling algorithms are throughput
optimal under dynamic channel and network conditions. However, these algorithms
achieve a hypothetical rate region which does not take into account the
overhead associated with channel probing and feedback required to obtain the
full channel state information at every slot. In this work, we design a joint
scheduling and channel probing algorithm by considering the overhead of
obtaining the channel state information. We adopt a correlated and
non-stationary channel model, which is more realistic than those used in the
literature. We use concepts from learning and information theory to accurately
track channel variations to minimize the number of channels probed at every
slot, while scheduling users to maximize the achievable rate region of the
network. More specifically, we employ Gaussian Process Regression that enable
us to predict future channel state and also provide a way to measure
uncertainty in the learning and prediction. Simulation results show that with
the proposed algorithm, the network can carry higher user traffic. The proposed
solution can be utilized in more complex and heterogeneous environments such as
5G and beyond networks where the model free approach is needed.
