## 2 OCC System Overview
### Pixel SINR  
There are two things that significantly affect the SINR for OCC. The first one is the interference generated by the neighboring light sources. The second one is the image distortion effects originating from the motion of the users, unfocused images, bad weather (e.g., rain, snowfall, fog, etc.), and long distance between the transmitter and the receiver. Considering these effects, the pixel SINR is represented as  
\[\text{SINR}=\frac{(1-\chi)\big{(}\xi\text{P}_{\text{av}}\text{H}_{\alpha,\beta }\,\sigma^{2}\big{)}^{2}}{\sum\limits_{\text{j}=0}^{\text{M}}\left(\xi\text{P} _{\text{av}}\text{H}_{\text{j},\beta}\,\sigma_{\text{j}}^{2}\right)^{2}+\text{ q}_{\text{e}}\xi\text{P}_{\text{avn}}\text{A}_{\text{c}}\text{f}_{\text{r}}},\ [\chi\in R:0\leq\chi\leq 1], \tag{6}\]  
where \(\text{P}_{\text{av}}\) is the average transmitted pixel intensity, \(\xi\), denotes the optical-to-electrical conversion efficiency per pixel, M is the total number of neighboring OCC transmitters, j is the sequence of a light source, \(\text{H}_{\text{j},\beta}\) is the corresponding DC gain received from the specific light source, \(\sigma_{\text{j}}\) is the total number of pixels illuminated by that light source, \(\text{P}_{\text{avn}}\) is the average noise power, \(\text{q}_{\text{e}}\) is the electron charge, \(\mathbf{R}\) is the set of all real numbers, and \(\text{f}_{\text{r}}\) is the sampling rate of the camera. \(\chi\) is defined as the image distortion factor that depends on the perspective distortion and lens-blur in the camera imaging process.  
Distortions in the image can be reduced to a great extent by accurately focusing the camera using a high-resolution image sensor. The interference generated from the neighboring light sources can be mitigated significantly by applying region of interest signaling [2]. In this technique, the LED light source is detected at a low frame rate. When a region of the LED source is detected, the frame rate is accelerated and the data is demodulated at a high speed. Another method, called selective capturing, can also be used to minimize interference [17]. In this approach, the LED region is captured selectively within the frame. By reducing the captured area, the probability of interference is minimized.  
The OCC system's capacity can be derived from [PERSON]'s capacity formula. According to [PERSON] et al. [18], the capacity of a camera-based communication depends on the deterministic nature of the perspective distortions and the Additive white Gaussian noise (AWGN)characteristics of the communication channel and is expressed as  
\[\text{C}_{\text{OCC}}=\text{f}_{\text{r}}\text{W}_{\text{s}}\log_{2}(1+\text{ SINR}), \tag{7}\]  
where \(\text{W}_{\text{s}}\) represents the spatial bandwidth denoting the number of information carrying pixels per camera image frame. The parameters that affect the capacity of an OCC system include camera resolution, sampling rate, image distortion factor, and rapidity of LED detection and classification. Among them, the camera sampling rate is considered as the principal factor affecting the data rate performance. Most of the conventional commercial cameras offer low sampling rates (around 30 frames per second). Hence, previous OCC demonstrations using rolling shutter cameras with low-frame rates and conventional intensity modulation schemes showed considerably low data rates [10; 19; 20; 21]. However, the OCC capacity can be significantly improved using cameras with high sampling rates. In addition, the effects of motion blur, background noise, and other interfering elements also limit the overall capacity of an OCC system. These problems can be mitigated by ensuring accurate object focus by the image sensor and by using high-resolution cameras with enhanced focal length.