# Machine Learning Applications for Short Reach Optical Communication
## 1 Introduction
These ML based MFI models are normally trained with phase constellation diagrams, which are suitable for both long-reach systems and short-reach systems with coherent receivers. However, the intensity modulation with direct detection is also widely used in short-reach communications. Hence, ML-based MFI technologies in short-reach optical communications also need to be able to recognize intensity-only modulation formats, such as non-return-to-zero (NRZ) and return to zero  
Figure 2: Machine learning applications in optical communications. (RZ) on-off keying (OOK), PAM-4 and PAM-8. Thus, asynchronous amplitude histograms (AAH) [72] and eye-diagram [65] based ML technologies have been proposed for the MFI in short-reach optical systems. In addition to the application of ML in the field of OPM and MFI, ML techniques are also promising solutions in the signal processing for short-reach optical communications. In optical communication systems, digital signal processing (DSP) is widely used to compensate various transmission impairments, such as the linear impairments (e.g., CD and PMD) [53; 73]. For example, the ML-based equalizer has been used as the digital part of a tunable optical signal processor for CD compensation, which can reduce the footprint and attenuation compared with the solution based on fiber Bragg grating and dispersion compensation fibers [74]. In addition to the linear impairments, the nonlinear effects, such as stimulated Brillouin scattering (SBS), four-wave mixing (FWM) and self-phase modulation (SPM), also limit the performance of high-speed optical communication systems. For example, the interactions between laser phase noise and CD generate equalization enhanced phase noise (EEPN) [75], which is challenging to compensate. Furthermore, impairments caused by optical/electrical components with nonlinear memory effects are also difficult to compensate [76]. Therefore, high-speed DSP has been developed for the compensation of nonlinear impairments, such as the Volterra nonlinear equalizers [77]. While Volterra equalizers are known to be effective for removing nonlinear distortions, the implementation and computation complexity is very high. In order to find an alternative solution, the ML-based equalization has been proposed to provide better system performance or reduced complexity with comparable performance [78], since ML techniques can effectively reduce nonlinear distortion and accurately estimate various key parameters in optical networks [58]. In even shorter-reach in-building/indoor OWC systems, the ML technique has also been widely studied. In both visible light communication (VLC) and near-infrared indoor OWC systems, the ML technique has been applied to improve the data transmission performance, mainly by suppressing the inter-symbol interference and nonlinear effects existing in the system [79; 80]. In addition, the ML technique has also been widely applied in OWC-based in-building/indoor positioning systems, where better positioning accuracy, as well as simultaneous positioning and receiver orientation estimations, have been realized [81]. In recent years, several reviews and surveys about ML or short-reach communications have been published, such as the ML for OPM and MFI [37], recent developments of shot-reach optical communications [1] and ML-based equalization for short-reach transmission [82]. Unlike the previous survey papers, our survey provides a comprehensive review focusing on various ML-based applications in short-reach communications, including ML-based OPM and MFI techniques, signal processing techniques and in-building/indoor OWC systems. Compared with [37], our paper focuses on short-reach communications, and we not only review ML-based applications for OPM and MFI, but also review the application of ML in signal processing and OWC in-building and indoor positioning.