Utilizing deep neural networks to extract non-linearity as entities in PAM visible light communication with noise

Abstract

Herein, we propose a novel entity extraction neural network (EXNN) with a newly designed sampling convolution kernel approach and a deep learning-based structure to differentiate noise in visible light communication (VLC) systems. In particular, EXNN is used to extract linear and nonlinear distortion in a received signal as an entity and compensate for the signal by removing it. First, we designed a deep learning structure tailored for VLC systems, used experimentation to validate our algorithm’s usability, and determined an appropriate range for the hyper-parameters that govern the PAM-8 system. Second, we compared our approach with existing finite impulse response (FIR) linear and Volterra nonlinear compensation algorithms via experiments. Below the hard-decision forward error correction (HD-FEC) threshold limit of 3.8×10−3, experimental results show that the use of the EXNN increased the operating range of the direct current (DC) bias and the voltage by ∼33.3% and by ∼50% under optimal conditions, respectively. Furthermore, under corresponding optimal power conditions, the proposed approach improved the Q factor of the VLC system by 0.36 and 1.57 dB compared with the aforementioned linear and nonlinear equalizers, respectively. To the best of our knowledge, this is the first time that a deep learning operator has been custom-designed for the VLC system and we have named the completely redesigned network with this sampling convolution kernel operator as EXNN.