# Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks

## TL;DR

This paper introduces a DPP-based learning framework that efficiently solves subset selection problems in wireless networks, specifically improving link scheduling by approximating optimal solutions with lower computational costs.

## Contribution

The paper presents a novel DPP-based learning method for subset selection in wireless networks, bridging machine learning and stochastic geometry to enhance efficiency.

## Key findings

- DPPL approaches the optimal solution for link scheduling
- Significantly reduces computational complexity
- Outperforms traditional optimization algorithms

## 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 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.