Optical Camera Communications with Convolutional Neural Network for Vehicle-toVehicle Links

Miguel Roque Soares, Neha Chaudhary, Elisabeth Eso, Othman Isam Younus, Luis Nero Alves, Zabih Ghassemlooy

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

Abstract

This paper describes a vehicle-to-vehicle (V2V) communication system, employing optical camera communications (OCC). The system comprises the light emitting diode (LED)-based taillights and a raspberry camera used as the transmitter (Tx) and the receiver (Rx), respectively. The sectorized taillights (i.e., Tx) are intensity modulated at different frequencies, and a convolutional neural network (CNN) at the Rx is used for scene analysis, the region of interest (RoI) selection, and symbol detection. Results show that, the system data rates are constrained by the camera frame rate and symbol duration. The link performance is dependent on the CNN training set and we show that, the use of CNN allows a robust implementation, able to provide response under multiple situations: taillight obstruction, variable link distances, and misaligned Tx-Rx. Furthermore, CNN enables multiple input multiple output (MIMO) signal detection without the need for dedicated training.

Original languageEnglish
Title of host publication2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728167435
ISBN (Print)9781728160511
DOIs
Publication statusPublished - 20 Jul 2020
Event12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020 - Porto, Portugal
Duration: 20 Jul 202022 Jul 2020

Publication series

Name2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020

Conference

Conference12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
CountryPortugal
CityPorto
Period20/07/2022/07/20

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