Mitigating nonlinear distortions of high-powered LEDs for VLC using deep neural networks

Visible light communication (VLC) is an emerging technology showing promising signs of being incorporated into 6G. VLC has challenges to overcome, mainly the nonlinear distortion from light-emitting diodes (LEDs), attenuation at high frequencies, and the presence of ambient light. This paper presents the hardware implementation of VLC with a deep neural network (DNN) based demodulator that can mitigate the nonlinear distortions and improve the received signal quality, especially when high-powered LEDs are used. Off-the-shelf available LEDs have different properties from different manufacturers, and developing a model of all the combined effects is intricate. Few machine learning (ML) based solutions have been presented to mitigate these hindrances like Autoencoders, but the difficulty is with their pre-training. The presented method uses DNN that can be used robustly without pre-training. It is tested with experimentally obtained data using high-powered, off-the-shelf available LEDs. The DNN is trained using the received training and data symbols that are transmitted simultaneously. The DNN has improved the bit error rate (BER) by recovering the data symbols that had undergone distortion. It is observed that the DNN can make up for decreased efficiency due to training symbols by improving the BER for higher-order modulations. It is also observed that the required BER performance can be achieved with fewer training symbols in the transmitted packet.