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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
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.
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.
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