# Machine Learning Applications for Short Reach Optical Communication
## 1 Introduction
Therefore, the ML algorithm is a promising solution to dynamic optical networks, with applications in optical performance monitoring [41; 42; 43; 44; 45; 46; 47; 48; 49], modulation format identification [50; 51; 52] and nonlinearity mitigation [53; 54]. In the control plane, ML techniques have been used to achieve the highly demand dynamic control, reconfiguration, and optical network virtualization [39]. For example, the support vector machines (SVM) and clustering algorithms have been employed for detecting soft failures, and data virtualization of optical networks [55]. Meanwhile, the software-defined networking (SDN) has become an important tool for network virtualization and a flexible network traffic controller in recent years. Therefore, ML techniques have been combined with SDN to solve routing and wavelength assignment problems in the WDM systems [56]. In the field of short-reach optical communication, the EON is a promising flexible grid technology that efficiently allocate the spectrum resource to satisfy the ever-increasing end-user bandwidth demand [57]. The application of EON makes the short-reach optical networks more transparent and dynamic. However, in the meantime, it also leads to more complicated network architectures and operations. Therefore, the real-time monitoring of miscellaneous channel impairments ubiquitous in the network, also known as OPM, is essential for the reliable operation and efficient management of such complex optical networks. Unfortunately, independently monitoring multi-channel is a difficult task because the effects of various impairments are usually mixed and they are almost physically indivisible [58]. Therefore, the use of ML techniques has been widely investigated in OPM, focusing on different key parameters, such as the chromatic dispersion (CD) and polarization-mode dispersion (PMD) monitoring [59; 60], fiber nonlinearity monitoring [61], bit error rate (BER) estimation [62], and optical signal to noise ratio (OSNR) monitoring [63; 64; 65; 66]. Another key requirement in short-reach optical communication is the ability to autonomously identify the optical modulation formats deployed. This is particularly required by the OPM units in the intermediate network nodes, because the algorithms used in these devices are dependent on the modulation format utilized [42]. Thus, reliable and accurate MFI is highly demanded. However, the analytical modeling and analysis of MFI in short-reach communication is challenging due to the high hardware cost and complex algorithms. On the other hand, ML algorithms can learn from historical data without the need of accurate analytical framework. As a result, we have also seen several attempts to apply ML methods for the MFI in short-reach optical communication networks [67; 68; 51; 69; 67]. When applying ML techniques for OPM and MFI, the requirements and focus in short-reach and long-reach scenarios are different. First, the ML-based OPM in short-reach optical communications need to be less complicated and have higher sensitivity. For example, the CD and OSNR estimations in short-reach systems require higher accuracy than long-reach due to the reduced transmission distance. In addition, the major impairments that can affect the BER are different. Long-reach systems are mainly affected by the amplified spontaneous emission (ASE) noise, accumulated dispersion and nonlinear interference generated by the fiber Kerr effect [70; 71]. However, in many short-reach systems, optical amplifiers are not used, and hence, the dominating noise source is no longer ASE. Hence, the system configuration should be considered while determining which impairment parameters are the priorities, since nonlinearity, fiber dispersion, laser phase noise, and relative intensity noise (RIN) can all be the dominant impairment [1]. In terms of MFI, most ML-based MFI technologies are deployed in coherent communication systems to identify modulation formats, such as QPSK, 16-QAM and 64-QAM.