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Decoding scheme based on CNN for mobile optical camera communication.

Ke Yu, Jing He, Zheng Huang

|August 14, 2020

Summary

This summary is machine-generated.

A novel decoding scheme using a convolution neural network (CNN) significantly reduces errors in mobile optical camera communication (OCC). This CNN-based approach effectively handles stripe distortion, improving data transmission reliability in dynamic environments.

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Published on: March 13, 2017

Area of Science:

Optical Communications
Machine Learning
Image Processing

Background:

Mobile optical camera communication (OCC) systems face challenges with stripe distortion due to device movement.
Existing decoding schemes struggle to maintain high data integrity in dynamic mobile environments.
Bit error rates (BERs) are a critical metric for assessing the reliability of wireless optical communication.

Purpose of the Study:

To propose and experimentally validate a new decoding scheme for mobile OCC utilizing convolution neural networks (CNNs).
To enhance the robustness of OCC systems against stripe distortion in mobile scenarios.
To reduce bit error rates (BERs) in mobile OCC through advanced feature extraction.

Main Methods:

Development of a decoding scheme based on a convolution neural network (CNN) for image feature extraction.
Implementation of a controllable lateral and vertical mobile platform to simulate real-world mobile OCC conditions.
Experimental testing of the CNN-based scheme across various mobile speeds, ranging from 40 to 80 cm/s.

Main Results:

The proposed CNN-based decoding scheme effectively extracts features between bright and dark stripes, mitigating distortion.
At a mobile speed of 80 cm/s, the scheme achieved a BER of 3.8×10^-5 in the lateral movement case.
In the vertical movement scenario at 80 cm/s, the scheme demonstrated an even lower BER of 1×10^-5.

Conclusions:

Convolution neural networks offer a powerful solution for decoding in mobile optical camera communication systems.
The CNN-based approach significantly alleviates stripe distortion and reduces BERs, enhancing system reliability.
This method shows promise for practical implementation in high-speed mobile OCC applications.