# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 4 Experimental Setup  
A scenario comprising of multiband 5G NR at 3 GHz (20 MHz) and 10 GHz (20 MHz), which was mentioned in our prior work [54], was used to validate this technique, but no DPD was used. As an upgrade to this architecture, a multiband DPD block has been added to this arrangement to improve the speed of this link. A dual drive Mach Zehnder Modulator (MZM) operating with two separate RF-driven signals, and a 1310 nm DFB laser is used in the system depicted in Figure 11. The Vector Signal Generator (VSG) labelled as VSG1 transmits RF1, a 5G NR waveform at 10 GHz, while the 5G transceiver transmits RF2, a 3 GHz flexible waveform signal. The DPD process can be broken down into three stages. At first, these signals undergo up-conversion one by one at their respective carrier frequencies of 3 and 20 GHz, respectively, before passing them through 10 km of Standard Single-Mode Fiber (SSMF) and receiving them with a photodetector (0.71 A/W) that receives the signal and converts it back to the electrical domain. An amplification step is introduced since the multiband needs to be separated independently. The 10 GHz and 3 GHz signals are separated using a diplexer (DPX). After that, the signals are sent to several vector signal analyzers (VSA). For performance evaluation, each VSA output is passed to the post-processing block. This phase is done without DPD, which means the output is evaluated without going through the DPD process. The DPD method represented in Figure 4 is used in the second phase, referred to as the DPD training phase, and training is used until the error converges. To put it another way, DPD validates the theory behind inverting the amplitude and phase responses obtained at electrical amplifiers EA1 and EA2. CNN methods can be used depending on the user's needs and comparative requirements. We use the PRS (positioning reference signal) given in the 5G NR architecture to accomplish synchronization for the received waveforms (both, input and output). PRS is assumed to have a bandwidth of 20 MHz/106 resource blocks. In the time domain, the received and output reference broadcast signals are correlated, and the PDP (power delay profile) is processed through the maximum block to determine the strongest path of arrival. In the third step, the pre-distorted baseband signals are fed into the DPD block, where they are upconverted to their carrier frequency by their respective VSCs before being fed onto the optical connection. The signal obtained at the photodiode is then transmitted through a diplexer DPX to isolate the various multi-bands before being sent to the DPD training step. We flip the switches in the opposite direction during the DPD validation step. The evaluation for 5G NR frames is completed by predistortion and then transferring the frames to the VSG. Because the nonlinearities of the RoF link slowly change due to thermal impacts and component ageing, we conclude that real-time operation in the adaptation is unnecessary. Table 2 summarizes the parameters employed, which have previously been used in [1,2] and other state-of-the-art [54,55] studies. Table 3 displays the CNN parameters that result in NN performance that is optimised. Table 3 lists the parameters that define the architecture of the intended Convolutional Neural Network. The parameter selection is done by a trial-and-error test [1]. The table's final section assesses the NN's complexity by computing its expressions in terms of its coefficients. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \multirow{3}{*}{5G NR Waveforms} & \(f_{c}=3\) and \(10\) GHz \\  & Flexible O/G/F OFDM waveform \\  & Modulation = 256 QAM \\ \hline \multirow{3}{*}{Laser Diode} & \(\lambda=1310\) nm \\  & DD-MZM \\ \hline \multirow{3}{*}{Fiber} & SSMF \\  & Optical Fibre Dispersion = \(16\)\(\frac{\text{ps}}{\text{nmkm}}\) \\  & Length = \(10\) km \\  & Attenuation = \(0.44\)\(\frac{\text{dB}}{\text{km}}\) \\ \hline Photorecceiver & \(\mathcal{R}\) = \(0.69\) A/W \\ \hline \hline \end{tabular}
\end{table}