Nonlinear predistortion scheme based on Gaussian kernel-aided deep neural networks channel estimator for visible light communication system

A scheme for Gaussian kernel-aided deep neural networks nonlinear predistortion (GK-DNNPD), which could effectively reduce the computational complexity of the receivers, is experimentally demonstrated. Compared with lookup table (LUT) PD, the GK-DNNPD could increase the Q -factor of 8 pulse amplitude modulation visible light communication (VLC) system by 1.56 dB at 1.335 Gbps. We experimentally proved that GK-DNNPD could increase the bitrate under hard-decision forward error correction from 1.335 to 1.385 Gbps. This is the first time that GK-DNNs are utilized for PD in the field of VLC systems. Meanwhile, GK-DNNPD requires less data for training than LUT, and the space complexity of the model is lower than LUT as well, which provides GK-DNNPD with the potential to be applied in practical VLC systems.