# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 2 Literature Review  
In this section, only Machine Learning Methods are used to discuss the linearization methodologies that have been inferred for the RoF systems. Linearization of OFH has been a significant research area as summarized below in Table 1. There has always been a drive to have better linearization to get higher performance that has shifted the focus towards Machine Learning (ML). ML being the new avenue is the core discussion of this work so a recent literature review has been discussed for all methods with specific importance to machine learning methods. For the reduction of the RoF system impairments, a thorough literature review is described in Table 1. The table outlines the method used, the type of linearization used, the category, the parameters examined, and the benefits and drawbacks of each method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & **Author** & **Type** & **Subcategory** & **Parameter** & **Linearization** & **Advantages** & **Disadvantages** \\ \hline
1 & [PERSON] et al. [14] & KNN & ML & BER & Fiber nonlinearity & 0.6 dB improvement & Large training data required \\
2 & [PERSON] et al. [15] & SVM & Deep Learning & BER & Modulation Impairments & 1.3 dB improvement & High Complexity \\
3 & [PERSON] et al. [16] & SVM & ML & EVM & Fiber nonlinearity & 1.5 dB improvement & High data training and complexity \\
4 & [PERSON] et al. [17] & ML & AI & BER, OSNR & Cross Modulation Detection & N/A & N/A \\
5 & [PERSON] et al. [18] & ML & ML & OSNR & Phase Modulation & 1.4 dB improvement & N/A \\
6 & [PERSON] et al., [PERSON] et al. [19, 20, 21, 22, 23, 24] & ML & ML & EVM, ACLR & Laser Chirp & 10 dB improvement & Limited to small link lengths \\
7 & [PERSON] et al., [PERSON] et al. [25, 26, 27, 28] & Dig & ML-NN based & EVM & Laser Learns nonlinearities & Limited to LTE framework. Limited to 20 MHz \\
8 & [PERSON] et al., [PERSON] et al., [PERSON] et al. [29, 30, 31] & Dig & DNN & ACLR, EVM & Black Box approach & N/A & bandwidth and 256 QAM modulation \\
9 & [PERSON] et al.; [PERSON] et al. [33, 34] & Elec & Analog Predistortion & IMDS & Complete RoF system (Laser, photodiode, LNA) & IMDS for phase & Suppression of second & nonlinear distortion for high \\
10 & [PERSON] et al. [35] & Opt & Mixed Polarization & Second/third order nonlinear distortion & Complete RoF system & Suppression of second & \\  & & & & & & and third order & components are compressed. \\  & & & & & & & Transmission is wavelength \\
11 & [PERSON] et al. [36] & Opt & Dual wavelength & Second/third order nonlinear distortion & Complete RoF system & Suppression of second & dependent, i.e., nonlinear & components are compressed \\  & & & & & & nonlinearities & exclusively at anti-phased \\
12 & [PERSON] et al. [37] & Dig & DPD & Third order nonlinearities & Power Amplifier & Wideband & The DSP necessary is \\  & & & & & & improvement possible & difficult \\
13 & [PERSON] et al. [38] & Dig & DPD & ACLR, EVM & Laser & Added accuracy with & The amount of energy \\  & & & & & & less DSP requirements & consumed is enormous. \\
14 & [PERSON] et al. [39] & Dig & DPD & ACLR & Modulator & Higher suppression in & Feedback complexity. \\  & & & & & & ACLR by 15 dB & \\
15 & [PERSON] et al. [40] & Dig & Digital Post & & & distortion components & Digitter with high speed is \\  & & & & & & significantly & required.Uplinks are the \\  & & & & & & compressed. & only ones that apply. \\ \hline \end{tabular}
\end{table} 
Table 1: Nonlinearity mitigation methods employed (Dig = Digital, Elec = Electrical, Opt = Optical, ML = Machine Learning, ACLR = Adjacent Channel Leakage Ratio, EVM = Error Vector Magnitude are used to abbreviate in the table). \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No. ** & **Author** & **Type** & **Subcategory** & **Parameter** & **Linearization** & **Advanages** & **Disadvantages** \\ \hline
16 & [PERSON] et al. [41] & Dig & DPD & ACLR, EVM & Laser only & Less complexity and over & Development of DPP at the RRH \\  & [PERSON] et al.