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

• Main Authors: Affan, A. (Author), Asif, H.M (Author), Raahemifar, K. (Author), Tarhuni, N. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: article; artificial intelligence; beamforming; Beamforming; Bit error rate; Communications systems; Decoding; deep learning; Deep learning; High signal-to-noise ratio; Index modulation; light; Ma ximum likelihoods; maximum likelihood; Maximum likelihood; maximum likelihood method; Maximum-likelihood; Multiusers; new technologies used in massive MIMO; New technology used in massive MIMO; orbital angular momentum; Orbital angular momentum; Orthogonal frequency division multiplexing; Orthogonal frequency-division multiplexing; signal noise ratio; Signal receivers; Signal to noise ratio; simulation; theoretical study; Visible light communication; waveform
• Online Access: View Fulltext in Publisher

 Due to the growing number of users, power, and spectral effectiveness, most communication systems are complex and difficult to implement on a large scale. Artificial Intelligence (AI) has played an outstanding role in the implementation of theoretical systems in the real world, with less complexity achieving better results. In this direction, we compare the Non-Orthogonal Multiple Access (NOMA) technique for a multiuser Visible Light Communication (VLC) system with Successive Interference Cancellation (SIC) for two types of detectors: (1) the deep learning-based system and (2) the traditional maximum likelihood (ML) decoder-based system. For multiplexing, we compare the variations of novel Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (IM). In this article, we implement OFDM-IM and OAM-IM for four users for the Gaussian fading MIMO Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) VLC channels. The suggested systems’ bit error rate (BER) performances are compared in simulations for a wide range of Signal-to-Noise Ratios (SNRs), which shows that deep learning-based systems outperform the ML-based system for both users to ensure better decoding at the receiver end, especially at higher SNR values. The detection error is lower in a deep learning-based system at around 20% and around 30% for low SNR and high SNR values, respectively. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.