DSP optimization technique based on deep learning to account for the effects of fiber nonlinearity

Abstract

The first section of this work is the classification of digital signal processing (DSP) techniques for adjusting, minimizing, and utilizing fiber nonlinearities in coherent fiber optic transmission systems. Then, different experimental optimization methods based on the Volterra series of Deep Neural Networks (DNN) and the Digital Back Propagation (DBP) methods are contrasted with the aid of specific instances. Concrete examples reinforce the techniques. The findings demonstrate the viability of a DSP strategy using deep learning for inexpensive optical comms, resulting in significant performance gains and computational efficiencies. The optimal DSP, it is further determined, should balance minimizing transmission flaws with minimizing additional distortions brought on by ineffective linear or nonlinear correction steps as a result of in-line amplifier noise. It is demonstrated that, in addition to improving system performance, machine learning (ML) improves analytical reasoning, provides deeper mathematical comprehension of fiber nonlinearity correction, and advances theoretical knowledge in related fields.