Solar-CSK: Decoding Color Coded Visible Light Communications using Solar Cells
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908365285752832 |
|---|---|
| author | Wang, Yanxiang Yan, Yihe Hu, Jiawei Jiang, Cheng Kusy, Brano Uddin, Ashraf Hassan, Mahbub Hu, Wen |
| author_facet | Wang, Yanxiang Yan, Yihe Hu, Jiawei Jiang, Cheng Kusy, Brano Uddin, Ashraf Hassan, Mahbub Hu, Wen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solar-CSK: Decoding Color Coded Visible Light Communications using Solar Cells Wang, Yanxiang Yan, Yihe Hu, Jiawei Jiang, Cheng Kusy, Brano Uddin, Ashraf Hassan, Mahbub Hu, Wen Networking and Internet Architecture 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. |
| title | Solar-CSK: Decoding Color Coded Visible Light Communications using Solar Cells |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2505.10226 |