# Machine Learning Applications for Short Reach Optical Communication
## 1 Introduction
As a new type of communication technology, compared with conventional RF wireless technologies, the OWC has the advantages of realizing broadband transmission, flexible networking, low latency, anti-electromagnetic interference and high communication security [24; 25; 26]. Both the visible range and near-infrared wavelength band have been studied in OWC systems, and both line-of-sight (LOS) and non-line-of-sight (NLOS) links have been explored [27]. In addition, the use of OWC technology for in-building and indoor user positioning has also been proposed and studied [28; 29; 30; 31]. Recently, instead of using dedicated photo-detectors (PDs), the use of optical camera as the receiver in in-building and indoor OWC systems has attracted intensive interests as well [32; 33; 34]. Using OWC in in-building and indoor environments, over Gbps wireless connections have been demonstrated [35; 36]. In the various types of short-reach optical communication systems and networks described above, the communication requirement becomes increasingly stringent and the complexity and requirements of the system increases substantially. To cope with the increasing complexity of various short-reach communication applications, the use of artificial intelligence (AI) technologies in short-reach optical communications has been proposed and widely studied. AI simulate sophisticated biological processes such as learning, extrapolating, and self-correction to enable computer programming to solve problems. In the past few decades, improving the performance of optical communication systems and networks through the application of AI-based technologies has become a field of extensive research, including the fields of transmission, switching, and network management. In particular, machine learning, as a branch of the AI learning method, has been widely studied as it enables the agent to improve its performance in future tasks through learning from the experience gained [37]. In general, there are four types of machine learning algorithms: supervised, unsupervised, semi-supervised and reinforcement learning. The most commonly used ML algorithm in the field of optical communication is the supervised learning, which uses known output features to derive the computational relationship between the input training data and the output data [38]. The neural networks as a supervised machine learning method, such as the artificial neural networks (ANN), convolutional neural network (CNN), and recurrent neural network (RNN), have been widely applied in optical communication systems and networks. The ML and neural networks have been used in different tasks in optical communication systems and networks. A summary is shown in Figure 2. At the system level, ML is mainly applied in the signal processing to optimize the signal reception and detection after propagating through the highly nonlinear optical communication channels. On the other hand, at the network level, optical networks are facing more challenges due to increasing data switching and routing. From the optical network point of view, the data plane and the control plane both contribute to the increased complexity [39]. The optical network transforms from the traditional static network to the dynamic network, with no predefined spectrum grid or modulation format, and with reconfigurable network topologies, such as the elastic optical networks (EON). Although the overall performance, flexibility and efficiency of the new dynamic network have been greatly improved, it is facing challenges in terms of network reliability, heterogeneity, latency, availability, complexity, quality assurance, capacity, etc. [38]. With the ability of learning and adapt to changes in the environment, the ML technology can well cope with the challenges brought by dynamic optical networks and can even deal with unforeseen scenarios when the ML algorithm is designed [40].