Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions

Abstract

Hybrid Polymer Optical Fiber and Visible Light Communication (POF-VLC) systems are emerging as a promising solution for high-speed, interference-free connectivity, especially in environments where traditional RF communication is constrained. This paper investigates key nonlinear impairments in POF-VLC systems, such as chromatic dispersion (CD), self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), and stimulated scattering, which severely degrade signal quality and limit transmission range. We review advanced modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Multitone Modulation (DMT), alongside traditional methods like Non-Return-to-Zero (NRZ) and On-Off Keying (OOK), evaluating their effectiveness in overcoming these challenges. Furthermore, the application of machine learning, particularly neural network-based equalizers like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted for their potential to enhance signal quality and system performance. This review emphasizes the transformative role these advanced strategies can play in optimizing hybrid POF-VLC systems, paving the way for their integration into high-demand communication environments. Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.