Hybrid frequency domain aided temporal convolutional neural network with low network complexity utilized in UVLC...

Area of Science:

• Optical Communications
• Signal Processing
• Artificial Intelligence

Background:

• Nonlinear distortion is a major challenge in underwater visible light communication (UVLC) systems.
• Balancing equalization performance with network complexity is crucial for practical UVLC applications.
• Existing deep neural networks often struggle with severe distortion, impacting system reliability.

Purpose of the Study:

• To propose a novel hybrid frequency domain aided temporal convolutional neural network (TFCNN) for post-equalization in CAP modulated UVLC systems.
• To address the tradeoff between equalization effectiveness and computational complexity in UVLC.
• To improve the bit error rate (BER) performance under significant nonlinear distortion.

Main Methods:

• A hybrid frequency domain aided temporal convolutional neural network (TFCNN) architecture was designed.
• An attention mechanism was incorporated into the TFCNN for enhanced feature extraction.
• The TFCNN was implemented as a post-equalizer in a coded-orthogonal-frequency-division-multiplexing (CAP) modulated UVLC system.
• Performance was evaluated based on equalization effectiveness and network complexity.

Main Results:

• The proposed TFCNN achieved superior equalization performance compared to standard deep neural networks.
• The TFCNN maintained a bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit (3.8×10^-3) under severe distortion.
• TFCNN demonstrated a significant 76.4% reduction in network parameter complexity.
• The equalizer remained effective where other methods lost performance.

Conclusions:

• The TFCNN offers an effective solution for compensating nonlinear distortion in UVLC systems.
• The proposed model provides a favorable balance between high equalization performance and reduced computational complexity.
• TFCNN represents a promising advancement for practical and reliable UVLC systems.