###### Abstract  
Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication. v 2021 24 August 2021  
photodiode. This configuration is categorized as Optical Camera Communication (OCC) and is being considered as an additional modality for VLC reception [7]. Camera communication finds a plethora of applications, particularly for mobile systems. For example, indoor positioning using cameras [8] significantly improves when using active transmissions from the ceiling LEDs, and vehicles can communicate with one another using built-in brake-light LEDs and cameras [9]. It can also open new opportunities for networking across nodes in vehicular platoons [10]. The key advantage of camera communication is that it doubles as a communication receiver. Cameras equip a lens which provides the receiver a 120-180 (depending on the lens type) FOV. This helps in retaining the signal within line-of-sight (LOS) under mobility. It is possible to multiplex information across multiple (LED) transmitters within the field-of-view (FOV) of the camera. By encoding information across multiple LEDs, the effective data rate can be increased using the spatial dimension and achieve multicast networking simultaneously. However, the key challenge in OCC is the low sampling rate of camera receivers. In OCC, the sampling rate corresponds to the image frame sampling rate or frame rate (FPS: frames-per-second). Video capture or frame-sampling in today's cameras have significantly improved, with a capacity of 240 FPS being available in almost every mobile (smartphone) camera. Cameras with frame rates of the order of 1000 FPS are also available, however, they are very expensive and not necessarily available as embedded devices, unlike mobile cameras. Even with such frame-rate advancements, which help improve traditional photography and video capture quality, the sampling rates are still limited to the order of 0.1-1 KHz. This significantly limits the practical data rates achievable by OCC, and the dependency is heavy on spatial multiplexing in order to improve the data rates. The other challenges in OCC are carried over from those in computer vision, such as impact of lighting conditions (day vs. night, morning vs. evening and sunny vs. cloudy), weather conditions (fog, rain or snow) and mobility (variable perspectives between transmitter and receiver) on the quality of LED signal registered on the camera image. We posit that given the fact that each data sample in OCC receiver is a camera image frame, frame-rate limitation essentially manifests as a quality reduction problem in computer vision. For example, camera sampling rates can vary slightly during capture and the discrepancies can result in lost signals or partial signals, which manifest as blur on the image. Mapping the signal to a binary 1 or 0 heavily depends on the demodulation algorithm, which is image processing and computer vision tools dependent.