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 3GPP Release 17 criteria.

Experimental Evaluation of Hybrid Fibre–Wireless System for 5G Networks

“…The suggested method is compared and the magnitude of the errors is presented in the form of error vector magnitude (EVM). We utilise GMP techniques as a baseline architecture to compare with our recent work [18].…”

“…We find that real-time operation in the adaptation is unnecessary since the nonlinearities of the RoF connection slowly change owing to thermal effects and component ageing. Table 2 lists the parameters used, which have previously been discussed in [1,2] and other state of the art [18] studies. Table 2.…”

“…The high quantization noise is caused by a 1-bit operation, which necessitates the use of a bandpass filter on the receiver side, increasing the complexity of these systems. As a result, the A-RoF system is a better alternative than D-RoF and S-DRoF [18] since it is simple, inexpensive and already has a widespread infrastructure.…”

“…Because of the limitations imposed by the optical and microwave components, analogue fronthaul applications are limited. Many linearization methods have been proposed to address these issues [4][5][6][7][8][9], but these methodological solutions have been questioned due to limited bandwidth, high method complexity and the requirement of returning the output signal to the base station (feedback), which is a lengthy process [18]. A-RoF suffers from signal degradation and has a network expansion range of only a few tens of kilometres.…”

“…The parameters that constitute the design of the planned convolutional neural network are listed in Table 3. A trial and error test is used to pick the parameters [18]. The table's last part computes the NN's expressions in terms of its coefficients to determine its complexity.…”