Autoencoder for Optical Intelligent Reflecting Surface-Assisted VLC System: From Model and Data-Driven Perspectives

Zou, Cong; Yang, Fang; Sun, Shiyuan; Zhang, Ying-Jun Angela; Song, Jian; Han, Zhu

doi:10.1109/jiot.2023.3304480

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Moreover, in [13], a study was conducted on an AE-driven OWC system, considering various orders of quadrature amplitude modulation. Further, the study presented in [14] proposed an AE-based optical intelligent reflecting surface (OIRS) system to jointly optimize the processing modules at transmitter, OIRS, and receiver in a visible light communication (VLC) link. The authors in [15] proposed a DL framework for emerging image sensor communication and verify its feasibility through performing simulation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based End-to-End Optical Wireless Communication Systems With Autoencoders

Safi,

Tavakkolnia,

Haas

2024

IEEE Commun. Lett.

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The utilization of neural network-based autoencoders (AEs) for the implementation of the physical layer in communication systems has recently emerged as a promising technique for achieving end-to-end optimization of communication links. However, applying conventional AE architecture to intensity modulation/direct detection optical wireless systems is challenging due to positive real-value constraint, eye safety standards, and the limited dynamic range of light sources. To address these issues, in this paper we propose a practical architecture, namely differential AE, that incorporates the concept of differential signaling. This approach allows the transmission of negative encoder output elements. In a shot-noise limited scenario, we evaluate and contrast the performance of differential AE, conventional AE, and a non-AE classical system, showcasing the superior bit-error rate achieved by the differential AE.

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Section: Introductionmentioning

confidence: 99%