A Novel Neural Network-Based Method for Decoding and Detecting of the DS8-PSK Scheme in an OCC System

Abstract

This paper proposes a novel method of training and applying a neural network to act as an adaptive decoder for a modulation scheme used in optical camera communication (OCC). We present a brief discussion on trending artificial intelligence applications, the contemporary ways of applying them in a wireless communication field, such as visible light communication (VLC), optical wireless communication (OWC) and OCC, and its potential contribution in the development of this research area. Furthermore, we proposed an OCC vehicular system architecture with artificial intelligence (AI) functionalities, where dimmable spatial 8-phase shift keying (DS8-PSK) is employed as one out of two modulation schemes to form a hybrid waveform. Further demonstration of simulating the blurring process on a transmitter image, as well as our proposed method of using a neural network as a decoder for DS8-PSK, is provided in detail. Finally, experimental results are given to prove the effectiveness and efficiency of the proposed method over an investigating channel condition.

Keywords

• artificial intelligence (AI)
• neural network-based decoder
• optical wireless communication (OWC)
• optical camera communication (OCC)
• dimmable spatial 8-phase shift keying (DS8-PSK)
• blur image processing
• vehicular communication