Adaptive determinantal scheduling with fairness in wireless networks

TL;DR

This paper introduces a new wireless network scheduling framework using determinantal point processes to ensure fairness, offering a mathematically elegant and computationally feasible alternative to traditional methods.

Contribution

It formulates a fairness-aware scheduling problem as a convex optimization over determinantal point processes, bridging machine learning advances with wireless communication.

Findings

Determinantal processes effectively model SINR-based network scenarios.

The proposed approach achieves fair resource allocation.

Demonstrates computational tractability of determinantal scheduling.

Abstract

We propose a novel framework for wireless network scheduling with fairness using determinantal (point) processes. Our approach incorporates the repulsive nature of determinantal processes, generalizing traditional Aloha protocols that schedule transmissions independently. We formulate the scheduling problem with an utility function representing fairness. We then recast this formulation as a convex optimization problem over a certain class of determinantal point processes called $L$-ensembles, which are particularly suited for statistical and numerical treatments. These determinantal processes, which have already proven valuable in subset learning, offer an attractive approach to network resource scheduling and allocating. We demonstrate the suitability of determinantal processes for network models based on the signal-to-interference-plus-noise ratio (SINR). Our results highlight the potential of determinantal scheduling coupled with fairness. This work bridges recent advances in machine learning with wireless communications, providing a mathematically elegant and computationally tractable approach to network scheduling.