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

2025-05-15

AI Summary

Tandem solar cells (TSCs) exhibit broadband spectral response, complicating color-shift keying (CSK) signal decoding in visible light communication (VLC), while conventional optical filters severely compromise energy harvesting efficiency. Method: This paper proposes a filter-free CSK decoding scheme leveraging the intrinsic coarse-grained wavelength selectivity of commercial TSCs to construct a multi-channel photovoltaic receiver without external filters. A pilot-aided bidirectional long short-term memory (Bi-LSTM) network is designed to model and compensate for nonlinear channel distortions arising from residual spectral overlap among subcells. Contribution/Results: The method demonstrates robust performance across variable link distances (0.5–3 m) and ambient illumination levels (0–1000 lux). Experimental results show an order-of-magnitude reduction in average bit error rate (BER) compared to conventional least-squares (LS) channel estimation, while simultaneously enabling high-speed data reception and efficient energy harvesting—achieving synergistic optimization of dual functionalities.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Decoding color-coded VLC signals using broadband solar cells
• Overcoming spectral overlap in tandem solar cell-based receivers
• Improving bit error rates without energy-limiting color filters

Innovation

• Tandem solar cells decode color-coded signals
• Bidirectional LSTM framework infers channel characteristics
• COTS solar prototype reduces bit error rates