Demonstration of a low-complexity memory-polynomial-aided neural network equalizer for CAP visible-light communication with superluminescent diode
Holguin Lerma, Jorge Alberto
Ng, Tien Khee
Ooi, Boon S.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2020-08-24
Print Publication Date2020
Permanent link to this recordhttp://hdl.handle.net/10754/664812
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AbstractVisible-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.
CitationHu, F., A. Holguin-Lerma, J., Mao, Y., Zou, P., … Shen, C. (2020). Demonstration of a low-complexity memory-polynomial-aided neural network equalizer for CAP visible-light communication with superluminescent diode. Opto-Electronic Advances, 3(8), 200009–200009. doi:10.29026/oea.2020.200009
SponsorsThis work was supported in part by the National Key Research, Development Program of China (2017YFB0403603), and the NSFC project (No. 61925104). JAHL, YM, TKN and BSO gratefully acknowledge the financial support from King Abdullah University of Science and Technology (KAUST) through BAS/1/1614-01-01, REP/1/2878-01-01, GEN/1/6607-01-01, and KCR/1/2081-01-01. This publication is partially supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3417. JAHL further acknowledge access to the KAUST Nanofabrication Core Lab for the fabrication of devices.