光通信技术2024，Vol.48Issue(3)：57-63,7.DOI:10.13921/j.cnki.issn1002-5561.2024.03.010
弹性光网络中基于DRL的RMSA算法
RMSA algorithm based on DRL in elastic optical networks
摘要
Abstract
In order to better solve the routing,modulation format and spectrum allocation(RMSA)problems of elastic optical networks(EON),and further reduce the network blocking rate,an RMSA algorithm based on deep reinforcement learning(DRL)is proposed.This algorithm will consider two indicators,resource occupancy and spectral adjacency,which affect RMSA decision making in reward design,to encourage agents to prioritize selecting paths with low resource occupancy and high spectral adjacency to establish optical paths,and compare the performance of this algorithm with other algorithms in different networks.The simulation results show that compared with several typical DRL algorithms,the proposed algorithm has a lower network blocking rate.关键词
弹性光网络/路由、调制格式与频谱分配/网络阻塞率/深度强化学习/奖励设计Key words
elastic optical network/routing/modulation format and spectrum allocation/network blocking rate/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
侯临风,何荣希,吴梓敬..弹性光网络中基于DRL的RMSA算法[J].光通信技术,2024,48(3):57-63,7.基金项目
国家自然科学基金项目(61371091、61801074、62371085)资助 （61371091、61801074、62371085）
大连市科技创新基金项目(2019J11CY015)资助. （2019J11CY015）
