D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G & Beyond
D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G & Beyond
复制标题
D-RAN：面向6G及以后的基于DRL的需求驱动弹性用户中心RAN优化
DOI：
10.1109/tccn.2022.3217785
复制
发表时间：
2023-02
影响因子：
8.6
通讯作者：
Shahrukh Khan Kasi;U. Hashmi;S. Ekin;A. Abu-Dayya;A. Imran
中科院分区：
文献类型：
--
作者：
Shahrukh Khan Kasi;U. Hashmi;S. Ekin;A. Abu-Dayya;A. Imran
随着高度异构的应用需求，6 G及以后的蜂窝网络预计将是需求驱动的，弹性的，以用户为中心的，并能够支持多种服务。需要重新设计“一刀切”的蜂窝架构，以支持异构应用需求。虽然最近的几项工作提出了以用户为中心的云无线电接入网络（UCRAN）架构，这些作品没有考虑应用程序的异构性要求或用户的移动性。尽管已经报道了性能的显著增益，但是这些方法的固有刚性限制了它们满足未来蜂窝网络所期望的服务质量（QoS）的能力。本文通过提出一种智能的、需求驱动的、弹性的UCRAN架构来满足这一需求，该架构能够为各种用例提供服务，包括增强/虚拟现实、高速铁路、工业机器人、电子健康和更多应用。所提出的框架利用深度强化学习来根据每个应用程序的异构需求调整以用户为中心的虚拟单元的大小。此外，所提出的架构是适应不同的用户需求和移动性，同时执行多目标优化的关键网络性能指标（KPI）。最后，数值结果验证了所提出的方法的收敛性，适应性和性能对元算法和暴力方法。
With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. While several recent works have proposed user-centric cloud radio access network (UCRAN) architectures, these works do not consider the heterogeneity of application requirements or the mobility of users. Even though significant gains in performance have been reported, the inherent rigidity of these methods limits their ability to meet the quality of service (QoS) expected from future cellular networks. This paper addresses this need by proposing an intelligent, demand-driven, elastic UCRAN architecture capable of providing services to a diverse set of use cases including augmented/virtual reality, high-speed rails, industrial robots, E-health, and more applications. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous requirements. Furthermore, the proposed architecture is adaptable to varying user demands and mobility while performing multi-objective optimization of key network performance indicators (KPIs). Finally, numerical results are presented to validate the convergence, adaptability, and performance of the proposed approach against meta-heuristics and brute-force methods.