Training Approaches in Supervised Learning for ICI Mitigation in Gridless Nyquist-WDM

Two training processes are explored for the supervised learning algorithms SVM and KNN, adapted to perform Inter-Channel Interference mitigation in a 3×16-Gbaud 16QAM Nyquist-WDM system. Experimental results showed BER improvement at low training lengths of ~3k symbols for both training processes.