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

TL;DR

This paper introduces an all-optical reservoir computing system using double-ring resonators optimized by reinforcement learning, achieving high accuracy in optical packet header recognition with reduced chip size.

Contribution

It presents a novel all-optical reservoir design with reinforcement learning optimization for enhanced packet header recognition performance.

Findings

Achieved word-error rates of 5*10^-4 and 9*10^-4 for 3-bit and 6-bit recognition.

Optimized double-ring resonators reach a global maximum delay-bandwidth product.

The system significantly reduces chip size while maintaining high accuracy.

Abstract

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*10-4 and 9*10-4 are achieved for 3-bit and 6-bit packet header recognition tasks, respectively, which are one order of magnitude better than the previously reported values.