As long-haul coherent-detection DWDM optical transmission systems push the limits of spectral and power efficiency, developing energy-efficient NN-based nonlinear equalizers (NLEs) is critical. One promising approach? Multi-Task Learning (MTL).
🔹 What is MTL?
MTL enables a single shared ML model to handle multiple related tasks, improving generalization while reducing computational complexity (CC). This simplifies training, enhances power efficiency, and allows NN-based NLEs to dynamically adapt to varying channel spacings and XPM levels—without retraining.
🔹 MTL for Nonlinearity Mitigation
✅ Self-Phase Modulation (SPM) & Cross-Phase Modulation (XPM) – Major distortion sources in DWDM systems.
✅ NN-based NLEs vs. Traditional Methods – NN models provide lower CC than conventional techniques like DBP or Volterra equalizers while maintaining strong performance.
✅ Flexible Adaptation – Unlike traditional approaches that require complex retraining, MTL-based NLEs can dynamically compensate for nonlinearities across various channel conditions.
📌 Our Approach
We developed an NN-based NLE architecture using 1D-CNN and biLSTM layers, processing DWDM channel data with different channel spacings. The result? A single model that adapts across multiple scenarios, reducing complexity and enhancing network efficiency.
💡 Why It Matters
As optical networks scale, MTL-driven NLEs pave the way for more adaptive, efficient, and scalable signal processing—pushing the boundaries of optical communication performance.
Curious to hear your thoughts! How do you see MTL shaping the future of AI-driven optical systems? Let’s discuss! 👇
#MachineLearning #NeuralNetworks #OpticalCommunications #MultiTaskLearning #ComplexityReduction #DeepLearning #AllegroProject #AI #SignalProcessing
