Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
This work tackles practical modulation and demodulation problems in OWC for future wireless systems, but it appears incremental as it builds on existing deep learning approaches.
The paper addresses implementation challenges in optical wireless communication (OWC), such as illumination control and signal-dependent channel properties, by proposing a deep learning framework for transceiver design, with feasibility verified through simulation.
Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.