Transmission capacity of wireless networks

TL;DR

This paper provides a comprehensive analysis of the transmission capacity in wireless networks using stochastic geometry, offering explicit formulas, extensions, and practical case studies to optimize network performance.

Contribution

It unifies and advances the transmission capacity framework with new models, extensions, and case studies, enhancing understanding of wireless network performance.

Findings

Explicit performance dependence on key parameters

Enhanced models with fading and multi-hop

Design insights for spectrum, interference, and power control

Abstract

Transmission capacity (TC) is a performance metric for wireless networks that measures the spatial intensity of successful transmissions per unit area, subject to a constraint on the permissible outage probability (where outage occurs when the SINR at a receiver is below a threshold). This volume gives a unified treatment of the TC framework that has been developed by the authors and their collaborators over the past decade. The mathematical framework underlying the analysis (reviewed in Ch. 2) is stochastic geometry: Poisson point processes model the locations of interferers, and (stable) shot noise processes represent the aggregate interference seen at a receiver. Ch. 3 presents TC results (exact, asymptotic, and bounds) on a simple model in order to illustrate a key strength of the framework: analytical tractability yields explicit performance dependence upon key model parameters. Ch. 4 presents enhancements to this basic model --- channel fading, variable link distances, and multi-hop. Ch. 5 presents four network design case studies well-suited to TC: i) spectrum management, ii) interference cancellation, iii) signal threshold transmission scheduling, and iv) power control. Ch. 6 studies the TC when nodes have multiple antennas, which provides a contrast vs. classical results that ignore interference.