Deep Learning Based Signal Detection for OFDM VLC Systems
Nurul Aini Amran, Mohammad Soltani, Mehrdad Yaghoobi, Majid Safari
Abstract
Visible light communication (VLC) has become increasingly popular and has sparked a wide interest from various research areas. In order to fully realize the potential of VLC and to provide seamless connectivity to users, the underlying channel model of the system must be carefully understood. However, in a practical environment which considers specific geometrical configurations of the network and user behavior effects, the optical channel can often become too complex to be modelled mathematically. In this work, we apply deep learning (DL) to design an effective signal detection scheme for an indoor VLC communication system. With the aid of DL, our system is able to learn directly from the transmitted and received symbols and can reliably detect the original transmitted signals during the real time implementation with only a limited instantaneous knowledge of the channel. The simulation results confirm that our model offers very close performance to the optimal maximum likelihood (ML) detection with perfect channel state information (CSI). We also consider specific indoor environments (e.g., using a hotspot model) to confirm the robustness of the learning based schemes in different indoor scenarios.