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Resource slicing and customization in RAN with dueling deep Q-Network

机译：RAN与Dueling Deep Q-Network的资源切片和定制

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摘要

The emerging future generation 5G technology is expected to support service-oriented virtualized networks where different network applications provide unique services. 5G networks have the potential to allow completely different slices to co-exist in a substrate network and satisfy the differentiated requirements of various users. In networks with heterogeneous traffics, operators are required to provide services in isolation since each operator has its own defined performance requirements. However, achieving an efficient resource provisioning mechanism for such traffics is very challenging. This paper proposes a coarse resource provisioning scheme and a dynamic resource slicing refinement scheme based on dueling deep reinforcement learning for virtualized radio access network. Firstly, coarse resource provisioning scheme provisions and allocates radio resource to slices based on preferences and weights at different base stations. Secondly, reinforcement learning based slicing refinement adjusts the resource allocated to slices autonomously in order to balance satisfaction and resource utilization. The proposed dueling DQN algorithm unifies two objectives (QoS satisfaction and resource utilization) by weights to indicate the importance of each factor in the reward function. After the dueling DQN algorithm has output actions to provision resource at slice level, BS-level resource update is performed. Also, a common learning agent is used to control the activities of all the slices in the network. Then, a shape-based resource allocation algorithm is proposed to customize the diverse requirements of users to improve user satisfaction and resource utilization. Finally, a comprehensive performance evaluation is conducted against state-of-the-art solutions based on OFDMA air-interface design. The results reveal that the proposed algorithm balances satisfaction and resource utilization with 80% of the available resources. The algorithm also provides performance isolation such that, a sudden change in user population in one slice does not affect the others.
机译：新兴的未来一代5G技术有望支持面向服务的虚拟化网络，不同的网络应用提供独特的服务。 5G网络具有允许完全不同的切片在基板网络中共存并满足各种用户的差异化。在具有异构流量的网络中，运营商必须在隔离中提供服务，因为每个操作员都有自己定义的性能要求。但是，实现这种流量的有效资源供应机制非常具有挑战性。本文提出了一种基于Dueling Deeel加强学习的粗鲁资源供应方案和用于虚拟化无线电接入网络的DEALING DEEL加强学习的动态资源切片细化方案。首先，基于不同基站的偏好和权重，粗地资源供应方案规定并将无线电资源分配给切片。其次，基于加强学习的切片细化，调整为自主分配给切片的资源，以便平衡满意度和资源利用率。提议的Dueling DQN算法统一了两个目标（QoS满意度和资源利用率），以指示奖励功能中每个因素的重要性。在Dueling DQN算法在切片级别提供资源的输出动作之后，执行BS级资源更新。此外，共同学习代理用于控制网络中所有切片的活动。然后，提出了一种基于形状的资源分配算法来定制用户的各种要求，以提高用户满意度和资源利用。最后，根据OFDMA空气接口设计，对最先进的解决方案进行了全面的绩效评估。结果表明，所提出的算法将满意度和资源利用率均衡，80％的可用资源。该算法还提供性能隔离，使得一块切片中用户群的突然变化不会影响其他人。

著录项

  • 来源
    《Journal of network and computer applications》 |2020年第5期|102573.1-102573.13|共13页
  • 作者

    Sun Guolin; Xiong Kun; Boateng Gordon Owusu; Liu Guisong; Jiang Wei;

  • 作者单位

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China;

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China|UESTC Sch Comp Sci Zhongshan Inst Zhongshan 528400 Guangdong Peoples R China;

    German Res Ctr Artificial Intelligence DFKI GmbH D-67663 Kaiserslautern Germany;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Resource slicing; Resource allocation; Network virtualization; Dueling deep Q-network;

    机译：资源切片;资源分配;网络虚拟化;Dueling Deep Q-Network;

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