Meta-Learning-Based Multi-Task Framework for Joint Modulation Format Identification and ESNR Estimation in Coherent Optical Communication Systems

Optical performance monitoring is essential for adaptive and intelligent coherent optical communication systems. In this paper, a Transformer-based multi-task meta-learning framework is proposed for joint modulation format identification and electrical signal-to-noise ratio (ESNR) estimation from original received waveforms. A simulated coherent optical communication system is established to generate QPSK, 16QAM, and 32QAM signals under different launch-power conditions. The received I/Q waveforms are directly used as model inputs, avoiding handcrafted feature extraction or constellation-image conversion. The proposed model employs a shared one-dimensional Transformer encoder to extract temporal waveform representations. A prototypical classification branch is used for few-shot modulation format identification, while an ESNR regression branch is introduced for continuous signal-quality estimation. The two tasks are jointly optimized under an episodic support-query training mechanism. Experimental results show that the proposed method achieves 99.99% modulation identification accuracy on the test episodes. For ESNR estimation, the model obtains an MAE of 0.1194 dB, an RMSE of 0.1738 dB, and an R2 value of 99.83%. These results demonstrate that the proposed framework can simultaneously provide accurate modulation decisions and reliable ESNR estimation, showing its potential for waveform-based optical performance monitoring.