论文标题
基于深入的学习安排，并具有连续资源分配的下一代蜂窝系统
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems
论文作者
论文摘要
安排在多用户无线通信中起关键作用，因为各种用户的服务质量在很大程度上取决于分配的无线电资源。在本文中，我们提出了一种基于深度强化学习（DRL）的连续频域资源分配（FDRA）的新型调度算法，该算法共同选择用户并分配资源块（RBS）。调度问题被建模为Markov决策过程，DRL代理确定在每个RB分配步骤中应为该用户安排哪个用户以及该用户的连续数量。对状态空间，行动空间和奖励功能的设计精细来训练DRL网络。更具体地说，最初的准连续动作空间是连续的FDRA固有的，被改进为有限且离散的动作空间，以获得推理潜伏期和系统性能之间的权衡。仿真结果表明，所提出的基于DRL的调度算法的表现优于其他代表性基线方案，同时具有较低的在线计算复杂性。
Scheduling plays a pivotal role in multi-user wireless communications, since the quality of service of various users largely depends upon the allocated radio resources. In this paper, we propose a novel scheduling algorithm with contiguous frequency-domain resource allocation (FDRA) based on deep reinforcement learning (DRL) that jointly selects users and allocates resource blocks (RBs). The scheduling problem is modeled as a Markov decision process, and a DRL agent determines which user and how many consecutive RBs for that user should be scheduled at each RB allocation step. The state space, action space, and reward function are delicately designed to train the DRL network. More specifically, the originally quasi-continuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a trade-off between the inference latency and system performance. Simulation results show that the proposed DRL-based scheduling algorithm outperforms other representative baseline schemes while having lower online computational complexity.
