Opportunistic Splitting for Scheduling Via Stochastic Approximation
The authors consider the problem of scheduling a wireless channel among multiple users. A slot is given to a user with a highest metric (e.g., channel gain) in that slot. The scheduler may not know the channel states of all the users at the beginning of each slot. In this scenario opportunistic splitting is an attractive solution. However this algorithm requires that the metrics of different users form independent, identically distributed (iid) sequences with same distribution and that their distribution and number be known to the scheduler. This limits the usefulness of opportunistic splitting. In this paper, they develop a parametric version of this algorithm. The optimal parameters of the algorithm are learnt online through a stochastic approximation scheme.