Routing Based on Deep Reinforcement Learning in Quantum Key Distribution-secured Optical Networks
- Resource Type
- Conference
- Authors
- Sharma, Purva; Bhatia, Vimal; Prakash, Shashi
- Source
- 2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) Advanced Networks and Telecommunications Systems (ANTS), 2023 IEEE International Conference on. :1-5 Dec, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Simulation
Decision making
Optical fiber networks
Routing
Deep reinforcement learning
Telecommunications
Quantum key distribution
quantum key distribution
optical networks
routing
- Language
- ISSN
- 2153-1684
Routing is a challenging problem in quantum key distribution (QKD)-secured optical networks (QKD-ONs) and involves the selection of an appropriate route that establishes a secure path between the QKD nodes for secret key distribution. Deep reinforcement learning (DRL) is a promising approach for solving decision-making problems in complex networking environments such as QKD-ONs. By leveraging the capabilities of DRL algorithms, the routing decisions can be optimized to enhance network performance. This paper proposes a DRL-based solution for routing in QKD-ONs that enables the routing agent to learn and adapt to changing network conditions by understanding the networking environment. The performance of the proposed scheme is compared with the baseline schemes on NSFNET in terms of blocking probability. Simulation results indicate that compared to the baseline schemes (shortest path (SP) and hop count (HC)), the proposed DRL-based routing scheme reduces the blocking by 14.31% and 8%, respectively.