# Optical Lensless-Camera Communications Aided by Neural Network
## 1 Introduction  
The high popularity of communication devices and the natural integration with new technologies, known as the \"internet of things\", optical camera communications (OCC) [1; 2] has become a promising technology in the field of visible light communications (VLC). In addition, IEEE 802.15.7r1 as the official standard of OCC further promotes OCC standardization development [3]. In the future, OCC is expected to be a candidate for VLC used in some low-speed communication services. One shortcoming of OCC is that the optical components of the camera embedded in the electronic device greatly increase the thickness of the device, affecting overall appearance [4]. The camera thickness of most mobile devices currently on the market is greater than 5 mm [5]. If the optical components were removed without affecting communications, it would be possible to create ultra-thin cameras, which would facilitate the use of small cameras, endoscopes, pill cameras, and body microscopes. Moreover, the authors of [6] proposed a method for lensless photography, which eliminated the need for lenses by utilizing computation and coded apertures in front of the image sensor. Inspired by their research, we propose a new lensless-camera communication system model to achieve communications in a new and effective way. Figure 1a shows the proposed model of optical camera communications, which works in a half-duplex mode. The access point (AP) and terminal device both consist of a LED (as the transmitter) and a lensless camera (as the receiver). The transmitter can be a lighting LED light or the signal light of the communication device using white or any color. During communications, the up link (terminal to AP) only transmits control information. The model can be applied to some low-speed communication occasions, such as smart supermarkets or museums. Customers can use their electronic devices to download advertisements, news data, or obtain a URL link from the light source. For OCCs that use standard cameras, the image captured by the receiver contains a light source and its surrounding scenes. Accordingly, positioning or locating the light source on the image isimportant to decoding it, which currently is implemented by a template matching method [7]. However, when the terminal device and AP have relative movements during communications, the light source must be searched and extracted in a timely fashion; accordingly, template matching method becomes complex and inefficient. Additionally, interference lights and time-varying environments also degrade communication performance of OCC. In contrast, the decoding method used in [8], targets a stationary communication case. Interested in a new design for OCC, we propose a lensless camera-based OCC in this paper; we focus on decoding the special image captured by our camera. We designed an algorithm based on the back propagation (BP) neural network for image decoding. The captured images by our receiver are light spots on objects; accordingly, all the details are blurred. Accordingly, our neural network-based decoding algorithm can work efficiently. To make our decoding algorithm maintain better performance in time-varying environments, an adaptive training sequence adjustment mechanism was designed. Similar to [9], 4B6B coding and on-off keying (OOK) modulation were used, as shown in Figure 1b. Our main contributions can be summarized as follows: (1) We propose a novel optical camera communication model, which uses a lensless camera as the receiver to decrease the overall thickness of the device without affecting communications. (2) In consideration of the special features of the image captured by the lensless camera, we designed a decoding algorithm based on the BP neural network, which recognizes light source patterns in complex conditions (e.g., ambient light, bad weather, movement of communication devices). The simulation results show the reliable performance of our system.