# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
Currently, the artificial intelligence (AI) based approach has been adopted to enhance different performance features, for example, data rate, SNR, and communicating distance [11]. In the traditional scheme, the stream of data are converted into an optical signal being modulated using LEDs. Various modulation techniques have been proposed so far including both single-level intensity modulation schemes such as ON-OFF keying (OOK), under-sampled frequency shift ON-OFF keying (UPSOOK), and spectral-efficient transmission scheme techniques such as color shift keying (CSK) and multi-level intensity modulation. Afterwards, the modulated signal is sent through a wireless medium [12; 13; 14; 15; 16]. The transmitted data are captured in the image sensor receiver. Deep learning techniques, notably convolutional neural network (CNN), are employed at the receiver side to detect LED, and the data bits are retrieved using several image processing techniques. Recently, it has been reported that stripe distortion can be effectively reduced when CNN is employed to extract features from the bright and dark striated frames during decoding [17; 18]. The key issues that can characterize the OCC performance are broadly investigated in [19]. A new study have been conducted to improve decoding methods. They demonstrated differentiating multi-valued data more correctly for intelligent transport systems [20]. In addition, the authors have also demonstrated the variation of OCC performance based on the key performance parameters. Machine learning techniques at the receiver side have also been investigated due to the low computational complexity compared with the deep learning algorithms [21]. Authors in [22] used a support vector machine (SVM) in 8-superposed pulse amplitude modulation (8-SPAM) and direct-current-biased optical orthogonal frequency division multiplexing-based visible light communication (VLC) systems for LED detection purpose. SVM utilized optical barcode detection technique based on VLC have been studied in [23]. In [24], the authors have described SVM for constellation classification in two kinds of geometrically shaped 8-quadrature amplitude modulation for seamlessly integrated fiber and the VLC system. However, in all the traditional detection schemes, all the detected LEDs have been considered as data transmitting LEDs. In practice, many unwanted LED regions can be detected, which demolishes the robustness of the communication performance. For object detection purposes, CNN outperforms the other machine learning techniques. But, it cannot detect the actual transmitting LED regions deleting other unwanted LED regions from the camera frame because it does not consider spatial information of the object. Therefore, it fails to differentiate actual data transmitting LED from other unwanted LEDs [25]. Consequently, the data decoding process gets vulnerable in practical scenarios such as V2V and robotics in the presence of many LEDs in the field of view (FOV) of the camera. This seems to be a large roadblock towards the potential application of OCC technology. To the best of our knowledge, it is not investigated yet how a receiver can recognize actual data transmitting LED region from the frame among other unwanted LED regions and decode data intelligently from that accurate region in the OCC case. Therefore, in this paper, a novel intelligent camera receiver system employing SVM, a supervised learning model, along with CNN for OCC, has been designed. Our designed receiver system is capable of decoding data bits separating an accurate data transmitting LED region among various light-emitting sources present in the camera frame. Taking into account the visual flickering, we have modulated the optical beam by the FSOOK modulation technique, a low-cost and low-complexity modulation technique, which encodes bits into different frequencies [26]. This modulated optical signal was transmitted through a wireless medium to the receiver in the presence of other blinking LEDs.