# Machine Learning Applications for Short Reach Optical Communication
## 2 Machine Learning for OPM and MFI in Short Reach Optical Communications  
With the development of advanced optical communication systems and networks, the critical role of OPM in the system and network operation has emerged. OPM can optimize the system performance, shorten the system installation time, increase the transmission rate, and reduce operation costs. Advanced OPM methods also have the potential to enable fault management and enhance the quality of service (QoS) in the optical domain [83]. In optical communications and networks, the key parameters that need to be monitored in the optical domain include the total power, OSNR, CD, PMD and fiber nonlinearity [62]. Whilst the transmission distance is shorter, the OPM in short-reach needs to be less complicated and more sensitive. Recently, ML techniques, particularly the deep learning for implementing OPM have been proposed. Deep learning represents a ML method based on the concept of a deep and hierarchical representation of data, which can automatically learn the function of data at multiple levels of abstraction [84]. Once the deep learning algorithm, typically based on neural networks, is fully operational, the difference between the received system feature (including eye diagram, histogram, constellation diagram, power spectrum, and time/frequency domain signal) and the ideal data/ground truth can be attributed to specific physical parameters, which can be derived from previous training process, to realize the OPM function [85]. In addition, the parameters mentioned before (e.g., OSNR), MFI is an important parameter that needs to be monitored during the OPM implementation. Most of the non-machine learning technologies for MFI, such as AAH [86] and asynchronous delayed tap sampling (ADTP) [87], require additional hardware components, which can significantly increase the complexity and cost of implementation. Since neural networks can perform automatic feature extraction and conversion of the channel information required by MFI, the neural networks such as ANN, CNN, and RNN are also good candidates for MFI. Figure 3 summarizes the generic approach for conducting OPM and MFI using ML techniques. The first step is typically creating a data set for training the deep learning algorithm. Traditionally, the optical signal is converted to the electrical domain at the receiving end and then sampled to construct a data set. Another method to obtain the data set is directly monitoring the optical signal from the transmission link, such as measuring the OSNR using an optical spectrum analyzer (OSA), which does not require optical-to-electrical conversion and can be detected online without interrupting the regular data transmission. The second step is to extract the specific characteristics of the signal, which contains information about the impairments in the system or the modulation format. The eye diagram, power spectrum, constellation diagram, and histogram are examples of such features. These features are then employed in the final step for ML algorithm training to execute OPM, or MFI [37]. Currently, the training is typically implemented offline and  
after training, the trained model and feature extraction steps can be used for online OPM parameters estimation and MFI. During the system or network operation, the transmission conditions and the corresponding impairments may change over time, and hence, the ML models used may need to be retrained using a newly collected training dataset [88]. Therefore, the effective retraining of ML models is also highly desirable. The most popular deep learning is now based on the use of neural networks, so here we start with a summary of different neural networks that have been widely used in OPM and MFI in short-reach optical communications. ANN can be used to model the general relationship between the input and output data sets and are simple to implement [84; 89]. The initial application of ANN in OPM can be traced back to 2006, which was used for the OSNR monitoring.