光通信技术2024，Vol.48Issue(3)：23-29,7.DOI:10.13921/j.cnki.issn1002-5561.2024.03.005
基于深度强化学习的C+L波段弹性光网络频谱分配算法
Spectrum allocation algorithm for C+L band elastic optical networks based on deep reinforcement learning
摘要
Abstract
Aiming at the problem of intensified physical layer damage caused by stimulated Raman scattering(SRS)effect in C+L band elastic optical networks,a spectrum allocation algorithm based on deep reinforcement learning(DRL)adaptive modulation format is proposed.In the routing stage,the K-shortest routing algorithm is used to pre calculate K shortest candidate paths for business requests.In the stages of band,modulation format,and spectrum allocation,DRL is used for intelligent decision-making,and two reward functions are combined to reduce network blocking rate and improve spectrum utilization efficiency.The simulation results show that the algorithm can effectively reduce blocking rate and improve spectrum utilization.关键词
C+L波段弹性光网络/路由与频谱分配/受激喇曼散射效应/深度强化学习/奖励设计Key words
C+L band elastic optical network/routing and spectrum allocation/stimulated Raman scattering effect/deep reinfor-cement learning/reward design分类
信息技术与安全科学引用本文复制引用
晏丹,冯楠,左晓博,沈凌飞,任丹萍,胡劲华,赵继军..基于深度强化学习的C+L波段弹性光网络频谱分配算法[J].光通信技术,2024,48(3):23-29,7.基金项目
河北省硕士在读研究生创新能力培养资助项目(CXZ-ZSS2024101)资助. （CXZ-ZSS2024101）
