Survey of Hybrid Deep Learning Autoencoders for Enhanced Visible Light Communication Systems

Abstract

Visible Light Communication (VLC) systems, known for their high-speed data transmission and resistance to electromagnetic interference, face challenges like LED non-linearity, multipath distortion, and noise-induced signal degradation. To address these issues, deep learning approaches, particularly Autoencoders, have gained attention for enhancing VLC performance and reliability. This survey focuses on Hybrid Deep Learning Autoencoders (H-DLAEs) personalized for VLC systems. An overview of VLC technology is provided, highlighting its advantages and inherent challenges. It then explores the role of Deep Learning, emphasizing Autoencoders and their application in Communication Systems. The survey classifies existing H-DLAE models based on their architecture, including the integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture spatial and temporal features of VLC signals. Performance metrics such as Bit Error Rate (BER), data throughput, and adaptability to varying channel conditions are used to evaluate these Hybrid Autoencoders. This study also discusses practical implementations of H-DLAE in real-world VLC scenarios, analyzing their benefits and limitations. Finally, significant research gaps are identified, and future directions are suggested, emphasizing the role of hybrid deep learning models in advancing VLC systems. This survey aims to provide a comprehensive analysis of H-DLAE for VLC, serving as a valuable resource for researchers and practitioners at the intersection of Deep Learning and Optical Wireless Communication.