# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
###### Abstract  
This study describes an experimental realization using digital predistortion (DPD) for a fifth generation (5G) multiband new radio (NR) optical front haul (OFH) based Radio over Fiber (RoF) link. For the performance enhancement and complexity reduction of RoF links, a novel Convolutional Neural Network (CNN) based DPD technique is proposed, followed by comparisons with the generalised memory polynomial (GMP) based DPD method. To support enhanced mobile broad band scenario, the experimental testbed uses the 5G NR waveforms at 10 GHz with 20 MHz bandwidth and a flexible-waveform signal at 3 GHz with 20 MHz bandwidth. For 10 km of typical single mode fiber, a Mach Zehnder Modulator with two distinct radio frequency waveforms modulates a 1310 nm optical carrier utilizing distributed feedback laser. The error vector magnitude and number of estimated coefficients, and multiplications are all used to describe the experimental outcomes. The goal of the research is to see if CNN-based DPD improves performance while lowering complexity levels to meet 3 GPP Release 17 criteria. digital predistortion; convolutional neural network; radio over fiber; convolutional neural network; error vector magnitude +
Footnote †: journal:  
0000-0  
It is clear that utilising alternative techniques (D-RoF/SD-RoF) is time, resource and power consuming. As a result, the A-RoF systems are the preferable alternative for optical fronthaul due to their legacy, infrastructure, and cost-effectiveness (OFH). Mitigating nonlinearities in RoF transmission is critical for maximising the system's potential and has become worthy of discussion. To address the widespread challenges in all of these distinct fields, a variety of strategies have been employed. The ones widely used are mentioned in Section 2 under the literature review. As transmission quality diminishes, the laser nonlinearities become more significant. When it comes to long-range networks, however, nonlinearities caused by a combination of fiber dispersion and laser chirp are frequently the primary cause of impairments and degradation [7]. Due to the high peak-to-average power ratio (PAPR), Orthogonal Frequency Division Modulated (OFDM) signals, such as the highlighted 5G transmission, are also susceptible to similar distortions, i.e., PAPR. This study introduces nonlinear behavior and signal degradation compensation for OFH based RoF systems using 5G NR based RoF technology, to the best of the authors'  
Figure 1: Schema of RAN 5G covering the back and fronthaul with application scenarios such as houses, sports fields and transportation, etc. Figure 2: RoF system block diagram with Radio over Fiber and CNN based DPD system. Transmission of I/P and O/P across the general RoF link yields the RoF NN model \(\hat{I}\). After that, we backpropagate error through \(I\) to train \(I^{-1}\). The DPD-RoF model is then linearized by linking it to an RoF link. DPD is done in the digital baseband, which eliminates the need for DACs and ADCs The link that how CNN is made and trained is shown in Algorithm 1.  
knowledge. The novelty of this study is numerous, following a thorough literature assessment on nonlinearity mitigation in Section 2:  
1. In the experimental testbed, multiband 5G NR signals are used to cover enhanced mobile wide band (eMBB) scenarios and small cells for 3 GHz and 10 GHz, respectively. 2. An abled DPD method based on a Convolutional Neural Network (CNN) is proposed and demonstrated. In comparison to existing learning architectures, the proposed DPD identification approach has a better performance and lower complexity than our previous machine learning approach. 3. Finally, a simple CNN-based DPD algorithm is proposed as an upgrade to our previously published DPD-based technique based on deep learning for 20 MHz with 5G New Radio (NR) based RoF links. A new sort of training is used to implement the CNN DPD technique, which does not use In-direct Learning Architecture (ILA).