Analysis of a Proportionally Fair and Locally Adaptive spatial Aloha in Poisson Networks

TL;DR

This paper analyzes a family of spatial-Aloha protocols in Poisson networks, showing how local information-based controls can approximate the performance of full topology-aware schemes with practical implementability.

Contribution

It introduces a continuum of adaptive control schemes based on local stopping sets, bridging the gap between no topology and full topology information in spatial-Aloha.

Findings

Performance of local schemes approaches full information schemes as local info increases.

Closed-form expressions for various control schemes are derived.

Simulation confirms the effectiveness of local adaptive controls.

Abstract

The proportionally fair sharing of the capacity of a Poisson network using Spatial-Aloha leads to closed-form performance expressions in two extreme cases: (1) the case without topology information, where the analysis boils down to a parametric optimization problem leveraging stochastic geometry; (2) the case with full network topology information, which was recently solved using shot-noise techniques. We show that there exists a continuum of adaptive controls between these two extremes, based on local stopping sets, which can also be analyzed in closed form. We also show that these control schemes are implementable, in contrast to the full information case which is not. As local information increases, the performance levels of these schemes are shown to get arbitrarily close to those of the full information scheme. The analytical results are combined with discrete event simulation to provide a detailed evaluation of the performance of this class of medium access controls.