Error performance analysis in downlink cellular networks with interference management

Laila Hesham Afify, Hesham ElSawy, Tareq Y. Al-Naffouri, Mohamed Slim Alouini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Modeling aggregate network interference in cellular networks has recently gained immense attention both in academia and industry. While stochastic geometry based models have succeeded to account for the cellular network geometry, they mostly abstract many important wireless communication system aspects (e.g., modulation techniques, signal recovery techniques). Recently, a novel stochastic geometry model, based on the Equivalent-in-Distribution (EiD) approach, succeeded to capture the aforementioned communication system aspects and extend the analysis to averaged error performance, however, on the expense of increasing the modeling complexity. Inspired by the EiD approach, the analysis developed in [1] takes into consideration the key system parameters, while providing a simple tractable analysis. In this paper, we extend this framework to study the effect of different interference management techniques in downlink cellular network. The accuracy of the proposed analysis is verified via Monte Carlo simulations.

Original languageEnglish
Title of host publication2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages591-596
Number of pages6
ISBN (Electronic)9783901882746
DOIs
StatePublished - 6 Jul 2015

Publication series

Name2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015

Bibliographical note

Publisher Copyright:
© 2015 IFIP.

Keywords

  • Cellular networks
  • Equivalent-in-distribution
  • Interference management
  • Stochastic geometry
  • Symbol error probability

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Optimization
  • Modeling and Simulation

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