在6G网络中通过人工智能实现智能资源分配策略：集中式还是分散式？
Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?
摘要：
在本文中，我们设计了一种新的智能软件定义无线电接入网络（RAN）架构，具有灵活性和流量感知等重要属性，适用于第六代（6G）无线网络。特别是，我们认为一个分层的资源分配框架，提出的智能软RAN模型，其中软件定义网络（SDN）控制器是第一层的框架。该单元动态监测网络，根据分布式或集中式资源分配架构选择网络操作类型，智能决策。在本文中，我们的目标是使网络更具可扩展性和更灵活的可实现的数据速率，开销和复杂性指标。为此，我们引入了一个新的指标，吞吐量开销复杂性（TOC），建议基于机器学习的算法，这使得这些性能指标之间的权衡。特别是，基于TOC的决策是通过深度强化学习（DRL）来解决的，它确定了适当的资源分配策略。此外，对于选定的算法，我们采用软演员批评的方法，这是更准确，可扩展性和鲁棒性比其他学习方法。仿真结果表明，所提出的智能网络实现更好的性能相比，固定的集中式或分布式资源管理计划，缺乏动态的TOC。此外，我们提出的算法优于其他国家的最先进的网络设计中采用的传统学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
