Deep learning based transceiver design for multi-colored VLC systems.

By Hoon Lee, Inkyu Lee, Sang Hyun Lee. 2018

48 citations recorded by the source corpus.

This paper presents a deep-learning (DL) based approach to the design of multi-colored visible light communication (VLC) systems where RGB light-emitting diode (LED) lamps accomplish multi-dimensional color modulation under color and illuminance requirements. It is aimed to identify a pair of multi-color modulation transmitter and receiver leading to efficient symbol recovery performance. To this end, an autoencoder (AE), an unsupervised deep learning technique, is adopted to train the end-to-end symbol recovery process that includes the VLC transceiver pair and a channel layer characterizing the optical channel along with additional LED intensity control features. As a result, the VLC transmitter and receiver are jointly designed and optimized. Intensive numerical results demonstrate that the learned VLC system outperforms existing techniques in terms of the average symbol error probability. This framework sheds light on the viability of DL techniques in the optical communication system design.

Subfields: Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Tony Q. S. Quek
2. Sang Hyun Lee
3. Hoon Lee
4. Inkyu Lee

Citations in the corpus

• Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “The feasibility of such a projection layer has been verified in [15].”
• Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications (2018, methodology): “Since the binary constraint sb,j  {0, 1} is non-convex, deterministic operations including linear projections in [8] are no longer applicable to OOK systems.”
• Constrained Deep Learning for Wireless Resource Management (2019, background): “The feasibility of such a projection layer has been verified in autoencoder applications [14].”