基于深度强化学习的大规模MIMO系统联合QoS感知调度与预编码
Joint QoS-Aware Scheduling and Precoding for Massive MIMO Systems via Deep Reinforcement Learning
AI总结：
本文针对大规模MIMO系统中的联合用户调度、天线分配与预编码问题，提出基于深度强化学习的动态算法选择方法，通过效用函数整合QoS约束，仿真显示满意用户数较基线提升7.2%和12.5%。
摘要：
移动网络的快速发展使得第五代（5G）及超五代（B5G）移动网络对高数据速率、低时延和高可靠性应用的需求激增。与此同时，大规模多输入多输出（MIMO）技术对于实现这一愿景至关重要，并且需要与资源管理功能协同工作以提升用户体验。尽管传统的跨层自适应算法已被开发用于调度和分配网络资源，但在多样化的服务质量（QoS）要求和B5G特性下，所得规则复杂度较高。本文研究大规模MIMO系统中的联合用户调度、天线分配和预编码问题。与直接分配资源（如天线数量）不同，该分配过程被转化为基于深度强化学习（DRL）的动态算法选择问题，以实现高效的马尔可夫决策过程（MDP）建模和策略训练。具体而言，所提出的效用函数将QoS要求和约束整合到与MDP回报相匹配的长期系统级目标中。组件化的动作结构结合动作嵌入进一步将资源管理过程纳入模型。仿真结果表明，在严苛场景下，与静态算法选择及相关工作相比，满意用户数分别提高了7.2%和12.5%。
英文摘要：
The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios.
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。
