Empirical channel model of multiple-lanes dynamic vehicle-to-vehicle visible light communication system

Harpreet Singh Ghatorhe, Seong Ki Yoo, Thomas Statheros, Sujan Rajbhandari, Farah Mahdi Al-sallami

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

Abstract

Vehicle-to-vehicle visible light communication (V2V-VLC) channel gain is random due to the irregular shape of the vehicle headlight radiation pattern, dynamic traffic and variation of ambient noise at different times of the day. In this paper, we establish an empirical model of multiple-lanes V2V-VLC considering the variation of the received power (in dBm) as an indicator of the channel gain (in dB) on different lanes. We performed experimental received power measurements for a three-lane traffic system in a controlled environment based on realistic vehicle trajectories derived from a traffic dataset. The results show that the statistics of the channel gain do not differ on different lanes and when the vehicle changes lanes. The log-normal distribution closely fits the received power of the V2V-VLC system. The channel gain has a mean value of -78.0 dB on the middle lane, which is higher than the mean values on the right and left lanes, which are -78.5 dB.
Original languageEnglish
Title of host publication2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages115-119
Number of pages5
ISBN (Electronic)9798350348743
DOIs
Publication statusPublished - 23 Aug 2024

Publication series

NameInternational Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)
PublisherIEEE
ISSN (Electronic)2835-9038

Keywords

  • visible light commutations
  • vehicle to vehicle
  • improved genetic algorithm
  • interfering vehicles
  • MIMO

Fingerprint

Dive into the research topics of 'Empirical channel model of multiple-lanes dynamic vehicle-to-vehicle visible light communication system'. Together they form a unique fingerprint.

Cite this