Abstract

Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.

Original languageEnglish (US)
Article number4307136
JournalWireless Communications and Mobile Computing
Volume2018
DOIs
StatePublished - Jan 1 2018

Fingerprint

Resource allocation
Wireless networks
Monte Carlo simulation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Laplace functional ordering of point processes in large-scale wireless networks. / Lee, Junghoon; Tepedelenlioglu, Cihan.

In: Wireless Communications and Mobile Computing, Vol. 2018, 4307136, 01.01.2018.

Research output: Contribution to journalArticle

@article{6e76fe5915764506ad6707d19d729ef0,
title = "Laplace functional ordering of point processes in large-scale wireless networks",
abstract = "Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.",
author = "Junghoon Lee and Cihan Tepedelenlioglu",
year = "2018",
month = "1",
day = "1",
doi = "10.1155/2018/4307136",
language = "English (US)",
volume = "2018",
journal = "Wireless Communications and Mobile Computing",
issn = "1530-8669",
publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - Laplace functional ordering of point processes in large-scale wireless networks

AU - Lee, Junghoon

AU - Tepedelenlioglu, Cihan

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.

AB - Stochastic orders on point processes are partial orders which capture notions like being larger or more variable. Laplace functional ordering of point processes is a useful stochastic order for comparing spatial deployments of wireless networks. It is shown that the ordering of point processes is preserved under independent operations such as marking, thinning, clustering, superposition, and random translation. Laplace functional ordering can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions of such metrics are unavailable. Applications in several network scenarios are also provided where tradeoffs between coverage and interference as well as fairness and peakyness are studied. Monte-Carlo simulations are used to supplement our analytical results.

UR - http://www.scopus.com/inward/record.url?scp=85062729017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062729017&partnerID=8YFLogxK

U2 - 10.1155/2018/4307136

DO - 10.1155/2018/4307136

M3 - Article

AN - SCOPUS:85062729017

VL - 2018

JO - Wireless Communications and Mobile Computing

JF - Wireless Communications and Mobile Computing

SN - 1530-8669

M1 - 4307136

ER -