【摘要】 随着第五代通信技术(5G)的发展，各种应用场景不断涌现，而网络切片可以在通用的物理网络上构建多个逻辑独立的虚拟网络来满足移动通信网络多样化的业务需求。为了提高移动通信网络根据各切片业务量实现资源按需分配的能力，本文提出了一种基于深度强化学习的网络切片资源管理算法，该算法使用两个长短期记忆网络对无法实时到达的统计数据进行预测，并提取用户移动性导致的业务数据量动态特征，进而结合优势动作评论算法做出与切片业务需求相匹配的带宽分配决策。实验结果表明，相较于现有方法，该算法可以在保证用户时延和速率要求的同时，将频谱效率提高约7.7%。更多还原

【Abstract】 With the development of the 5th Generation Mobile Communication Technology(5G),various application scenarios continue to emerge. Network slicing can construct multiple logically independent virtual networks on a common physical network to meet the diverse service requirements of mobile communication networks. In order to enhance the ability of mobile communication networks to allocate resources on demand according to the traffic of each slice, this paper proposes a network slicing resource management algorithm based on deep reinforcement learning. The algorithm uses two Long Short-Term Memory(LSTM) networks to predict statistical data that cannot be reached in real time, and extracts dynamic characteristics of business data volume caused by user mobility, and then makes bandwidth allocation decisions that match the needs of slice services in combination with the Advantage Actor-Critic(A2C) algorithm. Experimental results show that compared with existing methods, this algorithm can improve the spectral efficiency by about 7.7% while ensuring the user’s delay and rate requirements.更多还原