Equalization based on artificial neural networks (NN) has proved to be an effective way for nonlinearity mitigation in various kinds of optical communication systems. In this Letter, we propose a novel methodology of dual-path neural network (DP-NN)-based equalization. By combining a linear equalizer with an input-pruned NN equalizer, DP-NN can effectively reduce the computation cost compared to a conventional NN equalizer. We confirm its feasibility through 4-ary pulse amplitude modulation (PAM4) transmission at a gross(net) bitrate of 160 Gb/s (133.3 Gb/s), based on a GeSi electro-absorption modulator operating at C-band. After a 2 km transmission, the bit error rate is below the 20% hard-decision forward-error-correction threshold of 1.5×10 with the DP-NN equalization, which outperforms the Volterra equalization and is comparable to conventional NN-based equalization.
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