# Machine Learning Applications for Short Reach Optical Communication
## 2 Machine Learning for OPM and MFI in Short Reach Optical Communications
For easier overview and discussion, we classify the above approaches into direct detection and coherent detection categories according to their receiver detection method. The typical application of ML models in OPM and MFI in short-reach optical communications is summarized in \[Z_{j}=f\left(\sum W_{ji}X_{i}\right)+bias \tag{1}\]5 At the transmitter side, the data to be transmitted is modulated \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**System Info.** & **Features Type** & **ML Techniques** & **Output Results** & **Advantages** & **Limitations** \\ \hline Direct detect & -Eye diagram & -ANN & -OSNR & -Simultaneous identification of CD PMD & -Need clock recovery -Feature engineering dependency \\
2 km link & -Eye histogram & & -PDD & & -Feedency \\
40 Gb/s DPSK & & & -PDD & & -Feedency \\ Sim. [84] & & & & & \\ \hline Coherent EON & -Fiber nonlinear interfer- & -ANN & -SNR & -ML-aided nonlinearity modeling & -Limited to few layers -High system complexity \\
35,70,90 GBd & -Power spectral density & & & & -High system complexity \\ \hline Coherent & -DAC sampled data & -DNN & -OSNR & -Learn from raw data -Save domain expertise & -Limited measurement range -Only considered ASE noise \\
64 GSa/s DAC & & & & & -Only considered ASE noise \\ \hline Direct detect & -AAH & -DNN & -MFI & -Multitask handling -High MFI and BRI accuracy & -Feature engineering dependency \\ Exp. [95] & & & -OSNR & -Fiber-high accuracy & -ANN-low accuracy -KNN-long training time -DT-complex structure \\ \hline Coherent & -DAC sampled data & -CNN & -OSNR & -Without feature engineering & -High complexity \\ \hline Direct detect & -Eye diagram & -CNN & -MFI & -Can handle complex raw eye diagram & -Feature engineering dependency \\ OOK & & & & -OSNR & -Fiber-high complexity \\ Exp. [65] & & & & & \\ \hline Coherent & -Constellation & -CNN & -OSNR & -Multi-functional & -Feature engineering dependency \\ \hline Coherent & -Frequency domain sampled data & -LSTM & -OSNR & -Nonlinearity-insensitive & -Feature engineering dependency \\ \hline \hline \end{tabular}
\end{table} 
Table 1: OPM and MFI applications based on machine learning. onto the optical carrier, where both single optical carrier or multiple optical carriers via WDM can be used. The optical carriers are normally generated by laser diodes (LDs) and modulated using Mach-Zehnder modulators (MZMs). When WDM is used, the multiple output signals are then combined by the multiplexer (MUX) and can be optionally amplified by Erbium-doped fiber amplifier (EDFA) before being transmitted through the fiber or fiber network (e.g., PON). During the fiber transmission, the optical signals experience different types of impairments, such as CD and PMD dispersion, cross-phase modulation (XPM), FWM and SPM. After fiber transmission, the data-carrying optical signal is then demultiplexed (if needed in WDM) and detected by the photodetector using either direct or coherent receiver. The converted electrical signal is then sampled for processing via the ML model. The ML studies can be split into two cases: the first case is using in-line data, such as OSNR, and the second case is using the received data at the receiver side. In the second case, most of the approaches require feature extraction generated diagrams such as the eye diagrams, histograms and constellation diagrams to train the ML algorithm. Some schemes do not require this step, where the raw received data are directly imported to the neural networks [92].