BiGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication

Abstract

Nonlinear devices and channel interference can significantly impact the received signal in visible light communication (VLC). While recent research has explored receiver recovery using deep learning, existing approaches often involve replacing traditional channel estimation and equalization modules with neural network models. However, these models introduce additional data processing steps after fast Fourier transform (FFT), leading to increased complexity. To address these challenges, this study introduces a novel direct time-domain waveform equalization approach using a bidirectional gated recurrent unit (BiGRU) neural network for indoor VLC employing direct current (DC)-biased orthogonal frequency division multiplexing (DCO-OFDM). Unlike previous methods, our proposed scheme utilizes time-domain waveform data from photodiode outputs for direct balancing, harnessing the potent nonlinear processing capabilities of the BiGRU model. We first analyze the nonlinear processing capacity of the BiGRU model and subsequently compare the performance of different receiving methods on a constructed indoor visible-light communication platform. Experimental results demonstrate that the BiGRU-based approach exhibits low complexity and exceptional nonlinear channel learning capabilities. Notably, the proposed method outperforms other strategies in terms of bit error rate without the need for pilot signals. These findings validate the potential of the BiGRU-based DCO-OFDM receiving scheme as a promising solution for future VLC systems.

Keywords

• BiGRU
• DCO-OFDM
• deep learning
• visible light communication
• nonlinear equalization