Autoencoder based underwater wireless optical communication with high data rate.

Area of Science:

• Optical Engineering
• Wireless Communication
• Signal Processing

Background:

• Underwater wireless optical communication (UWOC) offers high data rates and low latency.
• UWOC systems suffer from signal degradation due to photon absorption and scattering, leading to energy loss and inter-symbol interference.

Purpose of the Study:

• To address signal degradation challenges in UWOC systems.
• To develop a novel method for optimizing UWOC performance using machine learning.

Main Methods:

• Interpreting the UWOC system as an autoencoder (AE) named UWOC-AE.
• Utilizing the double Gamma function to approximate the underwater optical link's channel impulse response.
• Jointly optimizing the encoder and decoder within the AE framework to learn channel characteristics.

Main Results:

• The proposed UWOC-AE demonstrates superior performance in UWOC systems.
• Achieved high data rates compared to existing communication techniques.
• Effective learning of underwater channel characteristics through the AE model.

Conclusions:

• The UWOC-AE framework provides an effective solution for overcoming signal loss in UWOC.
• This approach enables higher data rates and improved reliability in underwater optical communication.
• Joint optimization within the AE framework is key to enhancing UWOC system performance.