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.

中文翻译：

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通过神经网络数字预失真增强分布式反馈标准单模光纤到光纤链路的性能

这封信提出了一种新颖的基于神经网络（NN）的数字预失真（DPD）技术，可消除光纤无线电（RoF）系统中的信号损伤和非线性。DPD通常使用基于Volterra的过程来执行，该过程利用了间接学习体系结构（ILA），该体系在计算上可能变得复杂且昂贵。所提出的使用NN的方法规避了与ILA相关的问题，并利用NN首先对RoF链路进行建模，然后通过在RoF NN模型中进行反向传播来训练基于NN的预失真器。此外，使用1310 nm分布式反馈激光器和标准单模光纤对长期演进的20 MHz 256 QAM调制信号进行了实验评估，从而建立了基于ILA的基于NN的RoF链路和基于Volterra的广义存储多项式的比较。通过报告相邻信道功率比，均方误差和误差矢量幅度来检查DPD的有效性。实验结果表明，NN-DPD令人信服地学习了RoF非线性，这可能不适合基于Volterra的模型，因此在计算开销和DPD性能方面可能会提供有利的权衡。