Complex-valued recurrent neural network equalizer with low complexity for a 120-Gbps 50-km optical PAM-4 IM/DD system

Abstract

This paper introduces a novel complex-valued recurrent neural networks equalizer (RNNE) designed for a 120-Gbps, 50-km optical 4-level pulse-amplitude modulation (PAM-4) intensity modulation and direct detection (IM/DD) system. By mapping adjacent symbols of PAM-4 signals onto the complex domain, the correlation between two adjacent symbols of PAM-4 signals can be preserved. Based on experimental results, the proposed complex-valued RNNE outperforms the traditional real-valued RNNE with a 1.38-dB system power budget gain at the 7% overhead forward error correction BER threshold of 3.8 × 10−3. We believe that complex-valued RNNE has an advantage over real-valued RNNE in processing real-valued signals in IM/DD systems.