论文标题
移动毫米波网络中基于学习的移交
Learning-based Handover in Mobile Millimeter-wave Networks
论文作者
论文摘要
毫米波（MMWAVE）通信被认为是未来蜂窝和无线网络中超高数据速率的关键推动力。基本站（BSS）和MMWave系统中用户之间的定向通信的需求是通过横梁成型实现的，这增加了通道估计的复杂性。此外，为了提供更好的覆盖范围，需要BSS的密集部署，这会导致经常移交并增加交往开销。在本文中，我们提出了一种共同解决波束形成和切换问题的方法。我们的解决方案需要使用最少数量的飞行员和支持移动方案的基于学习的切换方法进行有效的波束形成方法。我们使用强化学习算法来学习移动用户不同位置的备份BSS的最佳选择。我们表明，我们的方法在用户轨迹的所有位置提供了高率和可靠性，其切换数量很少。基于几何MMWave通道建模和实际建筑图数据的仿真导致室外环境显示我们所提出的解决方案在可实现的瞬时速率和轨迹速率方面的出色性能。
Millimeter-wave (mmWave) communication is considered as a key enabler of ultra-high data rates in the future cellular and wireless networks. The need for directional communication between base stations (BSs) and users in mmWave systems, that is achieved through beamforming, increases the complexity of the channel estimation. Moreover, in order to provide better coverage, dense deployment of BSs is required which causes frequent handovers and increased association overhead. In this paper, we present an approach that jointly addresses the beamforming and handover problems. Our solution entails an efficient beamforming method with a minimum number of pilots and a learning-based handover method supporting mobile scenarios. We use reinforcement learning algorithm to learn the optimal choices of the backup BSs in different locations of a mobile user. We show that our method provides high rate and reliability in all locations of the user's trajectory with a minimal number of handovers. Simulation results in an outdoor environment based on geometric mmWave channel modeling and real building map data show the superior performance of our proposed solution in achievable instantaneous rate and trajectory rate.
