We are excited to announce a recent publication in the Journal of Lightwave Technology:
S. Srivallapanondh, P. J. Freire, B. Spinnler, N. Costa, A. Napoli, S. K. Turitsyn, and J. E. Prilepsky,
“Parallelization of Recurrent Neural Network-Based Equalizer for Coherent Optical Systems via Knowledge Distillation,” J. Lightwave Technol., vol. 42, no. 7, pp. 2275–2284 (2024)
Access the paper via the journal’s website by searching “JLT, Volume 42 Issue 7, 2275”.
Key Contributions:
- Introduces a novel method using knowledge distillation to convert a powerful RNN-based equalizer (biLSTM + 1D-CNN) into a parallelizable feedforward model arXiv+13arXiv+13arXiv+13.
- Achieves a 38% reduction in inference latency, enabling real-time, low-latency deployment in coherent optical systems.
- Maintains near-optimal system performance, with only ~0.5 dB degradation in Q-factor.
Why this matters:
- Makes advanced AI-based equalization feasible in high-speed optical network hardware.
- Enhances throughput and scalability for coherent communication systems.
- Represents a significant step toward practical deployment of intelligent signal processing in photonics.