Abstract The Internet of Vehicles (IoV) allows connectivity between vehicles, infrastructure, and pedestrians in order to increase road safety. Due to the limitations of RF spectrums, it is expected that the ever-increasing demand for data transmission will not be met by RF alone in the near future. Therefore, researchers introduced the optical spectrum as a suitable candidate for a complementary solution to the RF spectrum. The widespread use of light-emitting diodes (LEDs) for illumination and cameras in vehicular environments provides an excellent opportunity for optical camera communication (OCC) in intelligent transport systems. The low frame rate in cameras limits the speed of information exchange in OCC technology. In this research, multiple input multiple output technology (MIMO) has been investigated to solve this challenge. In this research, we examine the challenges of a MIMO-OCC system in the transportation environment and then propose two solutions to solve these challenges. In this thesis, we consider a vehicular MIMO-OCC system model in which the traffic light LEDs transmit data streams separately in parallel channels to the camera of a vehicle. Since LEDs in the light sources are about a few millimeters apart, these parallel channels may disturb each other in MIMO-OCC systems. Some factors such as perspective, link distance, vehicle motion, lens blurring, bad weather conditions, noise, turbulence, and camera's internal factors can cause adjacent channels to interfere with each other and thus the interference between pixels increases. In this research, the challenges of vehicle motion, turbulence, and camera's internal factors are investigated on a MIMO-OCC system in the transportation environment. First, we employ point spread function (PSF) analysis to investigate the impact of turbulence on MIMO-OCC systems. For this purpose, the DND parameter is introduced to investigate the effect of strong turbulence, and the PSLED parameter is introduced to examine very strong turbulence on the interference between pixels. Also, we derive the effect of strong and very strong turbulence on the interference between pixels for various link distances, traffic light settings, and camera parameters. The results of the simulations show that in the case of no atmospheric turbulence, increasing the diameter of LEDs from 10 mm to 20 mm reduces the DND value for links of 20 m, 60 m, and 100 m by approximately 1, 6, and 8 digital numbers, respectively. In addition, the channel characteristics of MIMO-OCC systems in vehicular communications are very challenging due to the high mobility of vehicles. For the first time, this study theoretically describes the spread and sliding of the LED’s image on the camera sensor during an exposure time of the camera by applying the effect of the vehicle movement on the PSF of the OCC system in the transportation environment. As the vehicle moves, the image of the LEDs is spread onto the image sensor in the direction of the vehicle's movement. Therefore, by increasing the speed and angular speed of the vehicle, the interference between pixels increases, and the signal-to-noise ratio and the signal-to-interference and noise ratio decrease. A MIMO-OCC channel is implemented in the laboratory environment to eva‎luate the results of the simulations in this research. In this comparison, the experimental results are in good agreement with the simulation results. Subsequently, two solutions of spatial coding and deep learning are presented to reduce the effect of destructive factors on the efficiency of the vehicular MIMO-OCC systems in the transportation environment. Spatial coding can increase the length of the optical channel but reduces the data rate. Deep learning can also help detect LEDs and transmitted data, but increases the amount of processing at the receiver.

فروغ السادات طباطباء

اصغر غلامي , جمشيد ابوئي

محمد صدقي , مسعود عمومي