Solar-CSK: Decoding Color Coded Visible Light Communications using Solar Cells

TL;DR

This paper introduces a novel VLC decoding method using tandem solar cells and machine learning to accurately interpret color-coded signals without additional filters, enhancing energy harvesting and data decoding.

Contribution

It proposes a tandem solar cell design combined with an LSTM-based machine learning framework for improved VLC decoding without spectral filters.

Findings

Achieved robust VLC decoding performance with reduced bit error rates

Demonstrated effectiveness across different distances and lighting conditions

Utilized commercial solar prototypes for practical implementation

Abstract

Visible Light Communication (VLC) provides an energy-efficient wireless solution by using existing LED-based illumination for high-speed data transmissions. Although solar cells offer the advantage of simultaneous energy harvesting and data reception, their broadband nature hinders accurate decoding of color-coded signals like Color Shift Keying (CSK). In this paper, we propose a novel approach exploiting the concept of tandem solar cells, multi-layer devices with partial wavelength selectivity, to capture coarse color information without resorting to energy-limiting color filters. To address the residual spectral overlap, we develop a bidirectional LSTM-based machine learning framework that infers channel characteristics by comparing solar cells' photovoltaic signals with pilot-based anchor data. Our commercial off-the-shelf (COTS) solar prototype achieves robust performance across varying distances and ambient lighting levels, significantly reducing bit error rates compared to conventional channel estimation methods. These findings mark a step toward sustainable, high-performance VLC systems powered by the multi-layer solar technologies.