# Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications

## TL;DR

This paper explores the application of deep learning techniques to address design and implementation challenges in optical wireless communication systems, demonstrating a new DL framework for image sensor communication.

## Contribution

It introduces a deep learning framework tailored for optical wireless communication, specifically for image sensor communication, and discusses its feasibility and challenges.

## Key findings

- Feasibility of DL-based OWC system demonstrated through simulation.
- DL framework effectively addresses modulation and demodulation challenges.
- Discussion of technical and implementation issues for practical deployment.

## Abstract

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.