Asif Hafiz M, Affan Affan, Tarhuni Naser, Raahemifar Kaamran
Department of Electrical and Computer Engineering, Sultan Qaboos University, Muscat 123, Oman.
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA.
Sensors (Basel). 2022 Apr 4;22(7):2771. doi: 10.3390/s22072771.
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.
由于用户数量、功率和频谱效率不断增加，大多数通信系统都很复杂，难以大规模实施。人工智能（AI）在将理论系统应用于现实世界中发挥了突出作用，以较低的复杂度实现了更好的效果。在这个方向上，我们将多用户可见光通信（VLC）系统的非正交多址接入（NOMA）技术与两种探测器的连续干扰消除（SIC）进行比较：（1）基于深度学习的系统和（2）基于传统最大似然（ML）解码器的系统。对于复用，我们将新型轨道角动量（OAM）复用和带索引调制（IM）的正交频分复用（OFDM）的变体进行比较。在本文中，我们针对高斯衰落多输入多输出（MIMO）视距（LoS）和非视距（NLoS）VLC信道，为四个用户实现了OFDM-IM和OAM-IM。在一系列信噪比（SNR）的仿真中比较了所建议系统的误码率（BER）性能，结果表明基于深度学习的系统在两个用户方面均优于基于ML的系统，以确保在接收端有更好的解码，特别是在较高的SNR值时。在基于深度学习的系统中，对于低SNR值和高SNR值，检测误差分别约低20%和约30%。
