# Machine Learning Applications for Short Reach Optical Communication
## 5 Conclusions  
In this article, we have provided a comprehensive survey for the recent ML related research in short-reach optical communications (<100 km). This survey covers three fields of the state-of-the-art ML-based applications: OPM and MFI, signal processing and in-building/indoor OWC systems. The structure, advantages and limitations of these ML techniques have been discussed in detail. The emergence and development of ML techniques can save the cost of hardware and integration compared to non-ML techniques. In addition, ML-based OPM techniques are flexible and reconfigurable. Hence, it can adapt to dynamic and increasingly complex network structures and modulation formats. Furthermore, ML-based techniques have been developed to be no longer limited to a single function. In terms of OPM and MFI, some multi-functional approaches have been proposed to undertake the estimation of multiple OPM parameters and perform modulation formats classification simultaneously. In the field of signal processing, ML-based techniques are  
Figure 16: The structure of the proposed position and orientation estimation network. not limited to simple nonlinear equalization either. Auto-encoders that can optimize the system in an end-to-end manner are under study, and soft-demappers are under reach. In indoor OWC systems, the ML has shown excellent performance to solve the limitations such as pass loss, ambient noise and multipath propagation. For indoor OWC positioning systems, ML has been widely used to improve the position accuracy, realize simultaneous position and orientation estimations, and achieve the recognition of LED-IDs. However, the capability of many ML-based applications to handle feature extraction and complicated analysis is restricted, which normally require manual feature engineering. As a result, the ML techniques based on multi-layer neural network architectures, such as CNN, DNN, and LSTM, have recently been proposed to automatically learn features from raw data. Nonetheless, these approaches require a large amount of data for training, and some of them need pre-processing the raw data to improve accuracy. The combination of ML technologies and domain expertise offers predictive potential in the absence of big data sets or incompletely representative data, and it may become the critical study direction in the future. On the other hand, ML-based techniques are sensitive to network environment changes. System instability or reconfiguration can result in the requirement of retraining the ML model. How to perform real-time efficient retraining is also an important research topic for future exploration. Conceptualization, Y.X. and K.W. ; writing--original draft preparation, Y.X., Y.W. ; writing--review and editing, Y.X., Y.W., K.W. and S.K. ; supervision, K.W. and S.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Australian Research Council (DP170100268). Not applicable. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AAH & Asynchronous Amplitude Histogram \\ ACPR & Adjacent Channel Power Ratio \\ AdaBoost & Adaptive Boosting \\ ADAM & Adaptive Moment Estimation \\ ADTP & Asynchronous Delayed Tap Sampling \\ AI & Artificial Intelligence \\ ALSTM & Attention-Augmented Long Short-Term Memory \\ ANN & Artificial Neural Network \\ AOA & Angle of Arrival \\ AP & Access Point \\ ASE & Amplified Spontaneous Emission \\ BBU & Baseband Unit \\ BICM & Bit Interleaved Code Modulation \\ BiLSTM & Bidirectional Long Short-Term Memory \\ BRI & Baud Rate Identification \\ BRNN-SD & Bidirectional RNN based Soft Demapper \\ BtB & Back to Back \\ C-AE & Convolutional Autoencoder \\ CD & Chromatic Dispersion \\ CNN & Convolutional Neural Network \\ Ch & Channel \\ CV-QKD & Continuous-Variable Quantum Key Distribution \\ DAC & Digital-to-Analogue Converter \\ \end{tabular}