Comparison of support vector machine-based equalizer and code-aided expectation maximization on fiber optic nonlinearity compensation using a proposed BER normalized by power and distance index

Abstract

Advances in optimizing optical fiber communications have been on the rise these recent years due to the increasing demand for larger data bandwidths and overall better efficiency. Coherent optics have focused on many kinds of research due to its ability to transport greater amounts of information, have better flexibility in network implementations, and support different baud rates and modulation techniques. These result in fiber-optic lines to provide faster speeds to end-users. Recent literature has looked into further developing digital signal processing techniques, while others have focused on fiber material optimization. Machine learning is another area of research that has garnered traction due to such demands. This survey discusses support vector machine (SVM) and code-aided expectation-maximization (CAEM) techniques on how they compensate for nonlinearity in coherent fiber optical communications. The study mainly focuses on how these techniques impact the performance of the transmissions where they are implemented and how they compensate for fiber optic nonlinearity through either the reduction of bit error rates (BERs), the improvements in the quality factor, or through a suggested index based on BER, power, and distance. Collating the results and based on a distinctive index, SVM is preferable in mid-range haul transmissions while CAEM for longer hauls.