Memory-augmented neural network equalizer for short-reach optical interconnects

To address the severe intersymbol interference (ISI) in high-speed optical transmission systems, this paper proposes a memory-augmented neural network equalizer (MANNE). Building upon the architecture of a traditional neural network equalizer (NNE), the scheme introduces an external memory module based on a key-value structure, which dynamically stores and updates the central representations of various 4-level pulse amplitude modulation (PAM-4) symbols in the feature space and their associated uncertainties during network inference. Furthermore, a memory feedback mechanism is incorporated, enabling the MANNE to reference its memory during training, thereby enhancing its feature representation and generalization capabilities. To validate the effectiveness and practicality of the proposed scheme, C-band transmission experiments of a 128 Gbaud PAM-4 signal are carried out over a 2 km standard single-mode fiber (SSMF) transmission link. Experimental results show that at the KP4 forward error correction (KP4-FEC) threshold of 2.4E-4, the receiver sensitivity of MANNE is improved by approximately 1.0 dB compared to the traditional NNE. The Volterra nonlinear equalizer (VNLE) failed to reach this threshold, whereas MANNE not only meets the requirement but also reduces computational complexity by 72.2% compared with VNLE, achieving a good balance between performance and computational efficiency.