  In wireless networks, many problems can be formulated as subset selection
problems where the goal is to select a subset from the ground set with the
objective of maximizing some objective function. These problems are typically
NP-hard and hence solved through carefully constructed heuristics, which are
themselves mostly NP-complete and thus not easily applicable to large networks.
On the other hand, subset selection problems occur in slightly different
context in machine learning (ML) where the goal is to select a subset of high
quality yet diverse items from a ground set. In this paper, we introduce a
novel DPP-based learning (DPPL) framework for efficiently solving subset
selection problems in wireless networks. The DPPL is intended to replace the
traditional optimization algorithms for subset selection by learning the
quality-diversity trade-off in the optimal subsets selected by an optimization
routine. As a case study, we apply DPPL to the wireless link scheduling
problem, where the goal is to determine the subset of simultaneously active
links which maximizes the network-wide sum-rate. We demonstrate that the
proposed DPPL approaches the optimal solution with significantly lower
computational complexity than the popular optimization algorithms used for this
problem in the literature.
