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Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

CNN-LSTM-AM approach for outdoor wireless optical communication systems.

Montaser Abdelsattar¹, Eman S Amer², Hamdy A Ziedan^3,4

¹Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt. Montaser.A.Elsattar@eng.svu.edu.eg.

Scientific Reports

|September 1, 2025

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This study enhances vehicle-to-vehicle (V2V) communication using AI, significantly improving data throughput, power efficiency, and reducing errors and interference for reliable V2V systems.

Area of Science:

Electrical Engineering and Computer Science
Artificial Intelligence in Communications

Background:

Visible Light Communications (VLC) for vehicle-to-vehicle (V2V) systems face challenges like bit errors, high power consumption, and interference.
Existing V2V communication systems require enhancement for improved performance and dependability in dynamic environments.

Purpose of the Study:

To introduce an AI-based framework for enhancing VLC in V2V communication.
To address critical issues including power consumption, bit errors, and signal interference in V2V systems.

Main Methods:

A novel deep learning framework combining Convolutional Neural Networks (CNNs), Generative Adversarial Network (GAN), Gated Recurrent Unit (GRU), and Deep Denoising Autoencoder (DDAE) was developed.
The framework includes modules for power reduction (GAN), performance enhancement (GRU), Bit Error Rate (BER) reduction (DDAE), and interference cancellation (CNN-U-Net).

Keywords:

Bit error rate (BER)Convolutional neural network (CNNs)Deep denoising autoencoder (DDAE)Deep learning Gated recurrent unit (GRU)Generative adversarial network (GAN)

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Last Updated: Sep 9, 2025

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

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Simulations were conducted for V2V scenarios with varying longitudinal and lateral separations between vehicles.

Main Results:

The proposed CNN-U-Net-GAN-GRU-DDAE model demonstrated significant improvements in throughput, power efficiency, BER reduction, and interference cancellation.
The model achieved superior performance compared to existing models, outperforming CNN-U-Net, CNN-U-Net-GAN, and CNN-U-Net-GAN-GRU by 13.6%, 14.4%, and 4.2% respectively.
An overall average performance improvement of 31.7% was observed compared to previous works.

Conclusions:

The developed deep learning framework offers a reliable and scalable solution for enhancing V2V communication performance.
The integrated AI model effectively mitigates common issues in V2V systems, paving the way for more robust communication.