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.

Deep Learning for Optical Vehicular Communication

“…These extraneous light sources can have a significant impact on the system's bit error rate (BER). Additionally, the phenomenon of blur, either motion blur or blurring due to rainy, foggy, or snowy weather conditions, as what typically occurs in optical vehicular communication systems, was analyzed in [15]. In such cases, the VLC or optical camera commu-nication (OCC) receiver should perform region-of-interest (ROI) selection to detect the target transmitter, in order to reduce the amount of incident parasitic light.…”

“…For the first task, it is recommended to use the YOLO platform for realtime multiple-light-source tracking before the low-framerate camera is able to extract all the ROI information from actual transmitters. Then, for further explicit high-rate data communication, a neural network decoder [15] and an AIbased error correction (AIEC) method [21] are proposed to enhance the accuracy of the received data, particularly when the signal is distorted by a complex channel model. Further explanation and experimental results will be provided in the following sections.…”

“…We have previously described the blocks from which our proposed system is formed in other recent papers. Although some of these areas of work are complete, with correspondingly consistent results [15], others have thus far only been introduced as concepts or early-stage research [21]. Moreover, as AI-based modules demand a high computational power, typically achieved using graphics processing units (GPUs) to accelerate their performance, it is difficult to be fully confident in the benefits of AI technology, particularly when applied to very specific applications or standalone modules.…”

“…To achieve a higher pixel SNR, we could increase the received signal strength by either increasing the transmitter power or using a longer camera exposure time. However, neither solution is appropriate in this application, not only because power saving is also of a high priority in wireless communication systems, but also because increasing the camera's exposure time increases the probability of capturing fuzzy LED states, which occur when samples are obtained during pulse transitions [15].…”

“…Based on our research in [15], a sample image of an eight-LED group is used to analyze the effects of blur for different sizes of convolutional kernel. Simulation work also shows that the convolution-based mechanism results in blurring that flattens all LED intensities within an image.…”