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Updated: Sep 27, 2025

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

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Published on: September 25, 2020

Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems.

Hafiz M Asif¹, Affan Affan², Naser Tarhuni¹

¹Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat 123, Oman.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

Deep learning enhances Visible Light Communication (VLC) systems using Non-Orthogonal Multiple Access (NOMA) and Successive Interference Cancellation (SIC). AI-based detectors significantly reduce bit error rates compared to traditional methods, improving system performance.

Keywords:

beamforming deep learning maximum likelihood new technologies used in massive MIMO orbital angular momentum

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Area of Science:

Wireless Communication
Optical Communication
Artificial Intelligence

Background:

Complex communication systems face challenges in scalability and efficiency.
Artificial Intelligence (AI) offers solutions for implementing complex systems with reduced complexity and improved performance.
Visible Light Communication (VLC) is a growing technology with potential for high data rates and efficiency.

Purpose of the Study:

To compare the performance of deep learning-based detectors against traditional Maximum Likelihood (ML) detectors in a multiuser VLC system.
To evaluate the effectiveness of Non-Orthogonal Multiple Access (NOMA) combined with Successive Interference Cancellation (SIC) in VLC systems.
To analyze the impact of Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (IM) on system performance.

Main Methods:

Implemented and simulated a multiuser VLC system using NOMA and SIC.
Compared two detector types: deep learning-based and ML-based.
Utilized OFDM with Index Modulation (OFDM-IM) and OAM multiplexing (OAM-IM) for four users.
Evaluated system performance based on Bit Error Rate (BER) across various Signal-to-Noise Ratios (SNRs) for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) channels.

Main Results:

Deep learning-based systems demonstrated superior performance over ML-based systems in terms of BER.
The detection error was reduced by approximately 20% at low SNRs and 30% at high SNRs using deep learning.
The proposed systems showed better decoding at the receiver, especially at higher SNR values.

Conclusions:

Deep learning-based detection significantly improves the performance of multiuser VLC systems employing NOMA and SIC.
AI-driven approaches offer a more effective solution for complex communication systems compared to traditional methods.
The integration of advanced multiplexing techniques like OAM-IM and OFDM-IM with AI enhances the overall efficiency and reliability of VLC.