Demonstration of a low‐complexity memory‐polynomial‐aided neural network equalizer for CAP visible‐light communication with superluminescent diode

摘要

Visible-light communication(VLC)stands as a promising component of the future communication network by providing high-capacity,low-latency,and high-security wireless communication.Superluminescent diode(SLD)is proposed as a new light emitter in the VLC system due to its properties of droop-free emission,high optical power density,and low speckle-noise.In this paper,we analyze a VLC system based on SLD,demonstrating effective implementation of carrierless amplitude and phase modulation(CAP).We create a low-complexity memory-polynomial-aided neural network(MPANN)to replace the traditional finite impulse response(FIR)post-equalization filters of CAP,leading to significant mitigation of the linear and nonlinear distortion of the VLC channel.The MPANN shows a gain in Q factor of up to 2.7 dB higher than other equalizers,and more than four times lower complexity than a standard deep neural network(DNN),hence,the proposed MPANN opens a pathway for the next generation of robust and efficient neural network equalizers in VLC.We experimentally demonstrate a proof-of-concept 2.95-Gbit/s transmission using MPANN-aided CAP with 16-quadrature amplitude modulation(16-QAM)through a 30-cm channel based on the 442-nm blue SLD emitter.