聊城大学学报（自然科学版）2025，Vol.38Issue(5)：669-678,10.DOI:10.19728/j.issn1672-6634.2025010004
基于深度强化学习的光无线融合接入网端到端网络切片映射
End-to-end network slicing mapping for converged optical-wireless access networks using deep reinforcement learning
摘要
Abstract
In converged optical-wireless access networks,optical wavelengths are responsible for carrying wireless data,and their transmission rate in turn affects the allocation of computing and optical bandwidth resources.However,the independent scheduling for the optical and wireless network sides tends to be in-flexible,resulting in inefficient cooperation and utilization of optical and wireless resources.In this paper,we investigate the end-to-end(E2E)optical-wireless network slicing mapping problem in converged opti-cal-wireless access networks.To combine user requirements in the wireless side and radio access network(RAN)slicing scheduling in the optical side,we formulate an E2E network slicing mapping model and pro-pose a deep reinforcement learning(DRL)method.To enhance the decision-making process of DRL a-gent,a slicing request decomposition scheme is proposed,in which each slicing request is divided into two sub-requests.We evaluate the effectiveness of the proposed algorithm through simulations on large-scale 33-node networks.The results demonstrate the superiority of our proposed DRL over the heuristic algo-rithm,achieving an 34.4％reduction in large-scale networks.关键词
端到端光无线网络切片/无线接入网(RAN)切片/深度强化学习Key words
end-to-end optical-wireless network slicing/radio access network(RAN)slicing/deep rein-forcement learning分类
信息技术与安全科学引用本文复制引用
李若宇,望运武,顾家骅,朱敏..基于深度强化学习的光无线融合接入网端到端网络切片映射[J].聊城大学学报（自然科学版）,2025,38(5):669-678,10.基金项目
国家自然科学基金项目(62271135)资助 （62271135）
