Deep learning based end-to-end visible light communication with an in-band channel modeling strategy

Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED...

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Veröffentlicht in:Optics express 2022-08, Vol.30 (16), p.28905-28921
Hauptverfasser: Li, Zhongya, Shi, Jianyang, Zhao, Yiheng, Li, Guoqiang, Chen, Jiang, Zhang, Junwen, Chi, Nan
Format: Artikel
Sprache:eng
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Zusammenfassung:Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED-VLC systems. We present a deep learning-based autoencoder to deal with this challenge. A novel modeling strategy is proposed to bypass the influence of the LFN and other low signal-to-noise ratio data when training the channel model of our E2E framework. The deep learning-based autoencoder then embeds the differentiable channel model and learns to combat the majority of channel impairments. In the E2E LED-VLC experiment, 1.875 Gbps transmission is achieved under the 7% HD-FEC threshold, 0.325 Gbps faster than the baseline. The E2E framework is robust to signal bias and amplitude variations, implying dimming support in the indoor environment.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.464277