Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks
Abstract
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 NPhard and hence solved through carefully constructed heuristics, which are themselves mostly NPcomplete 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 DPPbased 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 qualitydiversity tradeoff 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 networkwide sumrate. 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.
 Publication:

arXiv eprints
 Pub Date:
 May 2019
 arXiv:
 arXiv:1905.00504
 Bibcode:
 2019arXiv190500504S
 Keywords:

 Computer Science  Information Theory;
 Computer Science  Machine Learning;
 Computer Science  Networking and Internet Architecture