Fast-convergence Physics-informed Correlation-enhanced Neural Network in DML-DD Link for Analog RoF Fronthaul
Conference: ECOC 2024 - 50th European Conference on Optical Communication
09/22/2024 - 09/26/2024 at Frankfurt, Germany
Proceedings: ITG-Fb. 317: ECOC 2024
Pages: 4Language: englishTyp: PDF
Authors:
Zhang, Yikun; Zhu, Yixiao; Man, Lina; Huang, Dangui; Zhuge, Qunbi; Hu, Weisheng
Abstract:
We propose a physics-informed correlation-enhanced neural network (CorrNet) for composite second-order distortion compensation in C-band DML-DD link. For 10-GHz 64-QAM signal A-RoF transmission over 15-km SSMF, CorrNet achieves 1-dB ROP sensitivity improvement compared with Volterra-based feedforward equalizer, and 72.7% epochs reduction compared with fully-connected NN.