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Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry

2024-06-18 · A. L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández, Óscar González de Dios, J. M. Rivas-Moscoso

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

📄 PDF Abstract BibTeX arXiv:2406.12602

Code (1)

alexgaarciia/packetopticallatencyrl 공식 구현

Tasks

Q-Learning

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Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

2026-04-03 · Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz, Michael König 외 arxiv

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Relational Deep Reinforcement Learning for Routing in Wireless Networks

2020-12-31 · Victoria Manfredi, Alicia Wolfe, Bing Wang, Xiaolan Zhang

While routing in wireless networks has been studied extensively, existing protocols are typically designed for a specific set of network conditions and so cannot accommodate any drastic changes in those conditions. For i…

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2024-07-08 · Federico Lozano-Cuadra, Mathias D. Thorsager, Israel Leyva-Mayorga, Beatriz Soret

This paper introduces an open source simulator for packet routing in Low Earth Orbit Satellite Constellations (LSatCs) considering the dynamic system uncertainties. The simulator, implemented in Python, supports traditio…