Packet Header Recognition Utilizing an All-Optical Reservoir Based on Reinforcement-Learning- Optimized Double-Ring Resonators

作者：Zheng Li, Xiaoyan Zhou, Zongze Li, Guanju Peng, Yuhao Guo, Lin Zhang · 发表于：IEEE Journal of Selected Topics in Quantum Electronics · 年份：2023 · DOI：10.1109/jstqe.2023.3307420 · 被引用次数：8 · 研究领域：Optical Network Technologies、Neural Networks and Reservoir Computing、Photonic and Optical Devices

Optical packet header recognition is an important signal processing task of optical communication networks. In this work, we propose an all-optical reservoir, consisting of integrated double-ring resonators (DRRs) as nodes, for fast and accurate optical packet header recognition. As the delay-bandwidth product (DBP) of the node is a key figure-of-merit in the reservoir, we adopt a deep reinforcement learning algorithm to maximize the DBPs for various types of DRRs, which has the advantage of full parameter space optimization and fast convergence speed. Intriguingly, the optimized DBPs of the DRRs in cascaded, parallel, and embedded configurations reach the same maximum value, which is believed to be the global maximum. Finally, 3-bit and 6-bit packet header recognition tasks are performed with the all-optical reservoir consisting of the optimized cascaded rings, which have greatly reduced chip size and the desired “flat-top” delay spectra. Using this optical computing scheme, word-error rates as low as 5×10-4and 9×10-4are achieved for 3-bit and 6-bit packet header recognition tasks, respectively, which are one order of magnitude better than the previously reported values.