In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs.
翻译:在本文中,我们设计了一个新的智能软件定义的无线电接入网络(RAN)架构,其重要属性包括:六代(6G)无线网络的灵活性和交通意识。特别是,我们考虑为拟议的智能软RAN模式建立一个分级资源分配框架,软件定义网络(SDN)控制器是该框架的首要层面。这个单位动态地监测网络,以便在分布式或集中式资源分配架构的基础上选择网络操作类型,以明智地执行决策。此外,在本文中,我们的目标是使网络在可实现的数据率、间接费用和复杂指标方面更加可扩展和灵活。为此,我们为拟议的机器基于学习的算法(TOC)引入一个新的衡量标准,即通过投入式间接费用复杂(TOC),使这些业绩指标相互取舍。特别是,基于TOC的决策是通过深度强化学习(DRL)来解决的,这决定了适当的资源分配政策。此外,我们使用软式的行为体激励法,在可实现更准确、可扩展性和更坚固性的数据率和复杂性指标方面。为此,我们为拟议的机器基于学习方式的算法的计算方法引入了一个新的衡量结果,在常规学习模式中,从而实现了使用智能的学习模式,在常规管理方法上缺乏。 将智能学习方法,在使用了其他思维模式上,在常规方法上,在使用了智能思维模式上缺乏。