Adaptive weight learning–aided synchronous multi-parameter monitoring of rate-flexible coherent optical access networks

Rate-flexible coherent passive optical networks (PONs) introduce heterogeneous physical-layer conditions and dynamic rate configurations across optical network unit (ONU) branches. These variations require robust joint monitoring of entropy and SNR. To address this issue, we propose an adaptive weight learning (AWL) method based on a convolutional neural network (CNN) for multi-parameter learning. Joint monitoring of entropy and SNR in a probabilistic constellation shaping (PCS)-based coherent transmission network is performed. A point-to-multipoint (PtMP) access system with a baud rate of 32 Gbaud and 40 km coverage is experimentally demonstrated over back-to-back, 26.7 km, and 40 km links, achieving data rates from 134.4 to 166.4 Gbps at entropies ranging from 4.2 to 5.2 bits. Dual-regression analysis in multi-parameter monitoring yielded lower mean absolute errors, reducing the entropy error by 0.028 bits and SNR error by 0.111 dB compared to single-parameter learning. Additionally, AWL demonstrated robustness and effectiveness across various scenarios, including mixed-modulation formats, extreme SNR conditions, chromatic dispersion, laser phase noise, transceiver nonlinearity, and supplementary upstream-oriented scenarios. Finally, the monitoring parameters are scalable to accommodate future optical access network demands.