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19 November 2019 Nonlinear predistortion scheme based on Gaussian kernel-aided deep neural networks channel estimator for visible light communication system
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Abstract

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.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2019/$28.00 © 2019 SPIE
Yiheng Zhao, Peng Zou, Meng Shi, and Nan Chi "Nonlinear predistortion scheme based on Gaussian kernel-aided deep neural networks channel estimator for visible light communication system," Optical Engineering 58(11), 116108 (19 November 2019). https://doi.org/10.1117/1.OE.58.11.116108
Received: 26 August 2019; Accepted: 31 October 2019; Published: 19 November 2019
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