Abstract

A decoding scheme based on a convolution neural network (CNN) is proposed and experimentally demonstrated in mobile optical camera communication (OCC). The CNN can be used to extract features between bright and dark stripes in images effectively. Thus, it can alleviate the stripe distortion in the mobile environment and reduce bit error rates (BERs) by using the proposed decoding scheme based on CNN. A controllable lateral and vertical mobile platform is built to simulate the mobile scenarios with different moving speeds (40–80 cm/s). The experimental results show that, at the moving speed of 80 cm/s, the proposed scheme based on CNN can achieve the BERs of ${3.8} \times {{10}^{- 5}}$ at the lateral case and ${1} \times {{10}^{- 5}}$ at the vertical case in a mobile OCC system.

© 2020 Optical Society of America

Full Article  |  PDF Article

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (6)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription