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Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link.

Seoyeon Oh¹, Minseok Yu¹, Seonghyeon Cho¹

¹Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

Machine learning enhances vertical-cavity surface-emitting lasers (VCSELs) for faster optical wireless communications. A bidirectional long short-term memory (Bi-LSTM) model achieved 13.5 Gbps in visible light communication.

Keywords:

long short-term memory (LSTM)machine learning (ML)optical wireless communication (OWC)visible light communication (VLC)

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Area of Science:

Optoelectronics
Wireless Communications
Machine Learning

Background:

Vertical-cavity surface-emitting lasers (VCSELs) are promising for optical wireless communications.
Off-the-shelf VCSELs have limited modulation bandwidth for multi-Gb/s data rates.
Machine learning (ML) is emerging as a solution for communication challenges.

Purpose of the Study:

To investigate the effectiveness of ML in enhancing VCSEL-based visible light communication (VLC).
To demonstrate a high-speed VLC link using ML with commercial VCSELs.

Main Methods:

Systematic analysis of ML techniques for VCSEL-based VLC.
Implementation of a bidirectional long short-term memory (Bi-LSTM) ML scheme.
Utilizing a commercial VCSEL device.

Main Results:

Demonstrated a high-speed visible light communication (VLC) link.
Achieved a data rate of 13.5 Gbps over a single channel.
This represents the fastest reported single-channel result from a cost-effective, off-the-shelf VCSEL.

Conclusions:

Machine learning, specifically Bi-LSTM, is highly effective in overcoming bandwidth limitations of VCSELs for VLC.
The demonstrated 13.5 Gbps data rate signifies a significant advancement for cost-effective, high-speed optical wireless communication systems.