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

Yanxiang Wang, Yihe Yan, Jiawei Hu, Cheng Jiang, Brano Kusy, Ashraf Uddin, Mahbub Hassan, Wen Hu

TL;DR

Solar-CSK investigates decoding color-coded CSK signals using a multi-material tandem solar-cell receiver to maintain energy harvesting while recovering color information. It introduces an anchor-based differential ML framework, specifically a bidirectional LSTM, to infer channel characteristics from solar-cell outputs in the presence of spectral overlap. A COTS solar prototype demonstrates improved BER across distances and ambient lighting compared to conventional channel estimation, validating a sustainable VLC path. The work outlines practical considerations and future directions with tandem cells to push toward higher data rates.

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.

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

TL;DR

Solar-CSK investigates decoding color-coded CSK signals using a multi-material tandem solar-cell receiver to maintain energy harvesting while recovering color information. It introduces an anchor-based differential ML framework, specifically a bidirectional LSTM, to infer channel characteristics from solar-cell outputs in the presence of spectral overlap. A COTS solar prototype demonstrates improved BER across distances and ambient lighting compared to conventional channel estimation, validating a sustainable VLC path. The work outlines practical considerations and future directions with tandem cells to push toward higher data rates.

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.
Paper Structure (25 sections, 11 equations, 25 figures, 1 table)

This paper contains 25 sections, 11 equations, 25 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. CIE 1931 Color Space
  4. Color-Shift Keying Modulation
  5. True Color Sensor vs Silicon Solar cell
  6. Tandem Solar Cells
  7. Color Separation with Tandem Cells
  8. Solar-CSK System Model
  9. Transmitter
  10. Receiver
  11. Demodulation
  12. Channel Estimation:
  13. Machine Learning:
  14. Implementation
  15. Evaluation
  16. ...and 10 more sections

Figures (25)

  • Figure 1: A multi-material solar array decodes CSK signals transmitted by RGB LEDs using machine learning.
  • Figure 2: (a) CIE color space. (b) CIE XYZ standard observer color matching functions.
  • Figure 3: IEEE 802.15.7 CSK constellations (blue dots).
  • Figure 4: Spectral response of the true color sensor AS73211.
  • Figure 5: Spectral response of a silicon-based solar cell.
  • ...and 20 more figures