Stochastic modelling of downlink transmit power in wireless cellular networks

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

This paper applies stochastic geometry to model the distribution of downlink transmit power in macro base stations in mobile networks. Using data from cellular 3G deployments in the UK, we find, through hypothesis tests, that the maximum transmit powers of base stations in urban environments are independent of both the local density of base stations and the transmit power of adjacent base stations. Following this, we propose using random assignment from a fixed probability distribution as a model for base station downlink transmit power assignment. This proposed method is shown to be suitable for base station power assignment via goodness-of-fit tests. We also study how different probabilistic and deterministic transmit power assignments to base stations affect the complementary cumulative distribution function of the SINR experienced by a user in the network.

Original languageEnglish
Title of host publication2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages33-37
Number of pages5
ISBN (Electronic)9781467381864
DOIs
Publication statusPublished - 25 Jan 2016
Externally publishedYes
Event20th IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2015 - Guildford, United Kingdom
Duration: 7 Sep 20159 Sep 2015

Publication series

Name2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2015

Conference

Conference20th IEEE International Workshop on Computer Aided Modelling and Design of Communication Links and Networks, CAMAD 2015
Country/TerritoryUnited Kingdom
CityGuildford
Period7/09/159/09/15

Keywords

  • Downlink transmit powers
  • Mobile networks
  • Real mobile network data
  • Spatial statistical analysis
  • Stochastic geometry

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