Methods for extracting V2V propagation models from imperfect RSSI field data

Silvija Kokalj-Filipovic, Larry Greenstein, Bin Cheng, Marco Gruteser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

We describe three in-field data collection efforts yielding a large database of RSSI values vs. time or distance from vehicles communicating with each other via DSRC. We show several data processing schemes we have devised to develop opportunistic Vehicle-to-Vehicle (V2V) propagation models from such data. The database is limited in several important ways, not least, the presence of a high noise floor that limits the distance over which good modeling is feasible. Another is the presence of interference from multiple active transmitters. Our methodology makes it possible to obtain, despite these limitations, accurate models of median path loss vs. distance, shadow fading, and fast fading caused by multipath. We aim not to develop a new V2V model, but to show the methods enabling such a model to be obtained from in-field RSSI data, without elaborate measurement design and the associated deployment cost. Finally, models based on field data allow for capturing the multiple effects of an increasing number of simultaneous V2V transceivers under typical extreme traffic scenarios.

Original languageEnglish (US)
Title of host publication2015 IEEE 82nd Vehicular Technology Conference, VTC Fall 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479980918
DOIs
StatePublished - Jan 25 2016
Event82nd IEEE Vehicular Technology Conference, VTC Fall 2015 - Boston, United States
Duration: Sep 6 2015Sep 9 2015

Publication series

Name2015 IEEE 82nd Vehicular Technology Conference, VTC Fall 2015 - Proceedings

Other

Other82nd IEEE Vehicular Technology Conference, VTC Fall 2015
Country/TerritoryUnited States
CityBoston
Period9/6/159/9/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Automotive Engineering
  • Hardware and Architecture

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