Experimental demonstration of deep learning-based HS2PSK-OFDM scheme for optical camera communication

This study proposes HS2PSK-OFDM, a hybrid waveform for vehicular systems using optical camera communication (OCC), which integrates spatial-2-phase-shift-keying (S2-PSK) and rolling-shutter orthogonal frequency division multiplexing (RS-OFDM). The proposed hybrid scheme enables simultaneous transmission of low-rate and high-rate data streams in OCC systems. The low-rate data stream facilitates the detection and tracking of light sources in vehicles to establish OCC links using the You Only Look Once (YOLO) algorithm. In contrast, the high-rate data stream transmits high-rate data, which are supported by region-of-interest (RoI) updates derived from the low-rate data stream. This process reduces the noise and computational costs of high-rate data streams in mobile environments. Deep learning techniques have also been proposed to improve the decoder performance of high data rate streams (RS-OFDM decoder) in highly mobile environments. This paper analyzes the technical considerations of the HS2PSK-OFDM scheme-based deep learning approach to validate its performance in mobile environments. In addition, the implementation results are presented to evaluate the feasibility of the proposed hybrid scheme.