Deep Reinforcement Learning based Decentralized Routing and Load-Balancing in Meshed QKD-Networks
Conference: ECOC 2024 - 50th European Conference on Optical Communication
09/22/2024 - 09/26/2024 at Frankfurt, Germany
Proceedings: ITG-Fb. 317: ECOC 2024
Pages: 4Language: englishTyp: PDF
Authors:
Johann, Tim; Kuehl, Sebastian; Pachnicke, Stephan
Abstract:
We present a deep reinforcement-learning (DRL) agent that routes encryption requests in a meshed QKD network autonomously in a decentralized manner without relying on a conventional shortest-path algorithm with linear weights. Our agent is able to reduce blocking of requests and simultaneously improves load-balancing.