论文标题
在mmwave mimo网络中进行波束形成的多代理双重Q学习
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks
论文作者
论文摘要
波束形成是毫米波（MMWave）多输入多输出（MIMO）通信的关键技术之一。设计适当的波束形式不仅可以提高接收信号的质量和强度，还可以帮助减少干扰，从而提高数据速率。在本文中，我们提出了一种分布式的多代理双重Q学习算法，用于在MMWave MIMO网络中进行光束成型，其中多个基站（BSS）可以自动，动态地调整其光束以服务于多个高度移动的用户设备（UES）。在分析中，考虑到UES的最大收到的电源关联标准，并考虑了现实的渠道模型。仿真结果表明，提出的基于学习的算法可以在详尽的搜索方面实现可比的性能，而在较低的复杂性下进行操作。
Beamforming is one of the key techniques in millimeter wave (mmWave) multi-input multi-output (MIMO) communications. Designing appropriate beamforming not only improves the quality and strength of the received signal, but also can help reduce the interference, consequently enhancing the data rate. In this paper, we propose a distributed multi-agent double deep Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple base stations (BSs) can automatically and dynamically adjust their beams to serve multiple highly-mobile user equipments (UEs). In the analysis, largest received power association criterion is considered for UEs, and a realistic channel model is taken into account. Simulation results demonstrate that the proposed learning-based algorithm can achieve comparable performance with respect to exhaustive search while operating at much lower complexity.
