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.

预计新兴的下一代5G技术将支持面向服务的虚拟化网络，其中不同的网络应用程序将提供独特的服务。5G网络有可能允许完全不同的切片共存于衬底网络中，并满足各种用户的差异化需求。在具有异构流量的网络中，由于每个运营商都有自己定义的性能要求，因此要求运营商隔离提供服务。但是，为此类流量实现有效的资源供应机制非常具有挑战性。针对虚拟化无线接入网的深度强化学习，提出了一种基于粗强化学习的粗略资源配置方案和动态资源划分细化方案。首先，粗略资源供应方案基于不同基站的偏好和权重来供应无线资源并将其分配给切片。其次，基于强化学习的切片优化会自动调整分配给切片的资源，以平衡满意度和资源利用率。所提出的决斗DQN算法通过权重统一两个目标（QoS满意度和资源利用），以指示奖励函数中每个因素的重要性。决斗DQN算法具有输出操作以在片级配置资源后，将执行BS级资源更新。而且，使用通用的学习代理来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，以定制用户的各种需求，以提高用户满意度和资源利用率。最后，针对基于OFDMA无线接口设计的最新解决方案进行了全面的性能评估。结果表明，所提出的算法使满意度和资源利用率与80％的可用资源保持平衡。该算法还提供了性能隔离，这样一个分区中的用户数量突然变化不会影响其他分区。