Analysis of complex extreme learning machine-based nonlinear equalizer for coherent optical OFDM systems

Abstract

One major drawback of coherent optical OFDM (CO-OFDM) is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Fiber nonlinearities can be mitigated using machine learning algorithms that are a nonlinear decision classifier. In this study, C-ELM based nonlinear equalizer is proposed for a MQAM CO-OFDM. MQAM CO-OFDM systems are simulated by designing a Monte Carlo simulation. In this simulation, the effect of fiber nonlinearities on received signals is demonstrated with constellation diagrams and results are given in form of BER-Fiber Length variations.