arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.05943eess.SP
收藏

mmWave MIMO网络波束形成的多智能体双深度Q学习

Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks

Xueyuan Wang, M. Cenk Gursoy

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。设计合适的波束形成不仅可以提高接收信号的质量和强度,而且可以帮助减少干扰,从而提高数据速率。在本文中,我们提出了一种分布式多代理双深度Q学习算法的波束形成在毫米波MIMO网络,其中多个基站(BS)可以自动和动态地调整其波束,以服务于多个高度移动的用户设备(UE)。在分析中,最大接收功率关联准则被认为是用于UE,并考虑到一个现实的信道模型。仿真结果表明,所提出的基于学习的算法可以实现相当的性能,而在穷举搜索,操作复杂度低得多。

英文摘要:

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.

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。

↑