Symbol error rate minimization using deep learning approaches for short-reach optical communication networks - Iqbal, Muhammad; Ghafoor, Salman; Ahmad, Arsalan; Aljohani, Abdulah Jeza; Mirza, Jawad; Aziz, Imran; Poti, Luca (Frontiers in Physics, 2024)
Short-reach optical communication networks have various applications in areas where highspeed connectivity is needed, for example, the inter-and intra-data center links, optical access networks, and indoor and in-building communication systems. Machine learning (ML) approaches provide a key solution for numerous challenging situations due to their robust decision-making, problem-solving solving, and pattern-recognition abilities. In this work, our focus is on utilizing deep learning models to minimize symbol error rates in short-reach optical communication setups.Various channel impairments such as nonlinearity, chromatic dispersion (CD), and attenuation are accurately modeled. Initially, we address the challenge of modeling a nonlinear channel.Consequently, we harness a deep learning model called autoencoders to facilitate communication over nonlinear channels. Furthermore, we investigate how the inclusion of a nonlinear channel within an autoencoder influences the received constellation as the optical fiber length increases.Another facet of our work involves the deployment of a deep neural network-based receiver utilizing a channel influenced by chromatic dispersion. By gradually extending the optical length, we explore its impact on the received constellation and consequently, the symbol error rate.Finally, we incorporate the Split-Step Fourier Method (SSFM) to emulate the combined effects of nonlinearities, chromatic dispersion, and attenuation in the optical channel. This is accomplished through a neural network-based receiver. The outcome of this work is an evaluation and reduction of the symbol error rate as the length of the optical fiber is varied.