Visible light communication (VLC) shows great potential in Internet of Vehicle applications. A single-input multi-output VLC system for Vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative (PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.

Underwater visible light communication (UVLC) is expected to act as an alternative candidate in next-generation underwater 5G wireless optical communications. To realize high-speed UVLC, the challenge is the absorption, scattering, and turbulence of a water medium and the nonlinear response from imperfect optoelectronic devices that can bring large attenuations and a nonlinearity penalty. Nonlinear adaptive filters are commonly used in optical communication to compensate for nonlinearity. In this paper, we compare a recursive least square (RLS)-based Volterra filter, a least mean square (LMS)-based digital polynomial filter, and an LMS-based Volterra filter in terms of performance and computational complexity in underwater visible light communication. We experimentally demonstrate 2.325 Gb/s transmission through 1.2 m of water with a commercial blue light-emitting diode. Our goal is to assist the readers in refining the motivation, structure, performance, and cost of powerful nonlinear adaptive filters in the context of future underwater visible light communication in order to tap into hitherto unexplored applications and services.