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

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.