Enhancing distributed feedback‐standard single mode fiber‐radio over fiber links performance by neural network digital predistortion

Abstract

This letter presents a novel neural network (NN) based digital predistortion (DPD) technique to obliterate the signal impairments and nonlinearities in radio over fiber (RoF) systems. DPD is generally performed with volterra based procedures that utilizes indirect learning architecture (ILA) that can become complex and expensive computationally. The proposed method using NNs evades issues associated with ILA and utilizes a NN to first model the RoF link and then training a NN based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for long term evolution 20-MHz 256-QAM modulation signal using 1310 nm distributed feedback laser, and standard single-mode fiber to establish a comparison between NN based RoF link and volterra based generalized memory polynomial using ILA. The efficacy of the DPD is examined by reporting adjacent channel power ratio, mean square error, and error vector magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a volterra based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

“…It will be interesting to see the comparison of NN DPD methodology with conventional GMP method that has been validated in [1,6,34]. MP/GMP are the most viable solutions that have been used for DPD.…”

“…With 5G in roll out stage in most part of the developed world, the microwave photonicsbased solutions such as Radio over Fiber (RoF) have a higher significance connecting the BBUs with RRUs [3][4][5] due to advantages such as cost effectiveness; immunity to electromagnetic disturbance, broader bandwidth and increasing the wireless links reach for all type of distances ranging from short to long. There have been various versions of RoF such as Analog Radio over Fiber (A-RoF) [6,7], Digital Radio over Fiber (D-RoF) [8,9], Sigma Delta Radio over Fiber (SD-RoF) [10][11][12] and other variants that have been proposed recently (see Figure 2). Up to an extent, A-RoF links are the simplest, uncomplicated and economical solution, however, they suffer from nonlinearities arising due to signal impairments and devices involved such as laser modules, fibers and photodiodes.…”

“…Given that we can do this in a simple and practical manner, the RoF technique proves to be extremely advantageous. There have been various versions of RoF such as Analog Radio over Fiber (A-RoF) [6,7], Digital Radio over Fiber (D-RoF) [8,9], Sigma Delta Radio over Fiber (SD-RoF) [10][11][12] and other variants that have been proposed recently (see Figure 2). Up to an extent, A-RoF links are the simplest, uncomplicated and economical solution, however, they suffer from nonlinearities arising due to signal impairments and devices involved such as laser modules, fibers and photodiodes.…”

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

“…Gu et al 65 proposed a classification ML algorithm for optical network monitoring and survivability in terms of quality of transmission estimation and failure management. Unlike previous review papers that present ML used in RoF applications 66,67,69 or higher‐layer optical networks, 68,70 our current survey is dedicated to the physical‐layer implementation of ML for O‐OFDM‐based networks. Since O‐OFDM has been recently reconsidered as a potential candidate for low‐complex and low‐latency communications (eg, 5G) due to the simplified associated DSP units and adaptivity to channel and transceiver bandwidth conditions, and is therefore crucial to analyze the impact of ML on the performance and complexity of such systems.…”

“…Gu et al 65 ML algorithm for optical network monitoring and survivability in terms of quality of transmission estimation and failure management. Unlike previous review papers that present ML used in RoF applications 66,67,69 or higher-layer optical networks, 68,70 our current survey is dedicated to the physical-layer implementation of ML for O-OFDM-based networks.…”